attck metrics

Tune_new.py

@@ -2,6 +2,7 @@ import torch
 import torch.nn as nn
 from torch.utils.data import DataLoader
 from sklearn.metrics import classification_report
+import copy
 
 # Framework and Utility Imports
 import SetUp
@@ -12,6 +13,8 @@ from architectures.Model import Model, Architecture
 from unlearning.CertifiedUnlearning import CertifiedUnlearning
 from unlearning.LinearFiltration import LinearFiltration
 from unlearning.WeightFiltration import WeightFiltration
+from eval.UnlearningAttack import UnlearningAttack
+from unlearning.Retrain import Retrain
 
 
 # Global Hyperparameters
@@ -147,8 +150,82 @@ def run_finetuning_or_baseline_eval(env_dict, run_training=False, lr_rate=0.0001
         print(f">> Skipping baseline log generation. Reason: {e}")
 
 
+# saves evaluation metrics to log files
+def log_metrics(evaluation_domains, reloaded, strategy_in_use):
+    
+    # Process and append metrics to target reporting paths
+    for domain in evaluation_domains:
+        print(domain["label"])
+        accuracy, report_dict = reloaded.evaluate(loader=domain["loader"], mode=domain["mode"])
+        Util._log_to_csv(
+            arch=ARCH.name,
+            mode=domain["mode"], 
+            accuracy=accuracy, 
+            report_dict=report_dict, 
+            strategy=strategy_in_use
+        )
+
+# performs MIA and ZRF attack on models and logs the results
+def run_unlearning_and_attack_eval(forget_train_loader, retain_test_loader, reloaded, strategy_in_use, suite_runner, device, forget_class):
+    """
+    Performs adversarial vulnerability stress tests (MIA and ZRF) in-memory 
+    on the freshly unlearned model instance without saving it to disk first.
+    """
+    if suite_runner is None:
+        raise ValueError("An active initialized UnlearningAttackSuite instance must be supplied.")
+
+    print(f"\n>>> Initializing Threat Model Stress Testing Suite for: {strategy_in_use}")
+    
+    # 1. Dynamically map the white-box feature extraction hook to the active inner model
+    suite_runner.register_model_hook(reloaded.model)
+    
+    # 2. Fire the complete evaluation suite using the isolated data split subsets
+    results = suite_runner.run_complete_evaluation(
+        target_class=forget_class,
+        framework_name=strategy_in_use,
+        forget_loader=forget_train_loader,      # Members split from the train data partition
+        retain_test_loader=retain_test_loader,  # Clean non-members split from validation data
+        device=device
+    )
+    
+    print(f"    [Attack Complete] Logit MIA AUC: {results['logit_mia_auc']:.4f} | "
+          f"Internal MIA AUC: {results['internal_mia_auc']:.4f} | "
+          f"ZRF Score: {results['zrf_score']:.4f}")
+    
+
+# performs MIA and ZRF attack on models and logs the results
+def run_shaddow_attack_eval(forget_train_loader, retain_test_loader, reloaded, strategy_in_use, suite_runner, device, forget_class):
+    """
+    Performs adversarial vulnerability stress tests matching the localized 
+    shadow architecture specifications laid out in thesis Section 5.5.
+    """
+    if suite_runner is None:
+        raise ValueError("An active initialized UnlearningAttackSuite instance must be supplied.")
+
+    print(f"\n>>> Initializing Threat Model Stress Testing Suite for: {strategy_in_use}")
+    
+    # Instantiate a clean copy of the baseline trained model to serve as the Shadow reference proxy
+    # (Since finetuning is done once, we read its parameters cleanly from disk)
+    base_shadow = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
+    base_shadow.load(arch=ARCH)
+    
+    # Execute the updated conditional attack framework
+    results = suite_runner.run_complete_evaluation(
+        framework_name=strategy_in_use,
+        target_class=forget_class,
+        forget_loader=forget_train_loader,      
+        retain_test_loader=retain_test_loader,  
+        unlearned_instance=reloaded,             # The unlearned candidate model
+        base_shadow_instance=base_shadow,        # The shadow proxy architecture
+        device=device
+    )
+    
+    print(f"    [Attack Complete] Adversary Binary Classification Accuracy: {results['mia_accuracy']:.4f}")
+    
+
+
 # Unlearning and strategy eval
-def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evaluate = False):
+def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evaluate = False, suite_runner=None):
     """
     Reloads a clean model state, applies the isolated unlearning framework,
     and runs specific target evaluation domain checks.
@@ -170,6 +247,9 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu
     reloaded = Model.create(arch=ARCH, device=device, size=CLASS_SIZE)
     reloaded.load(arch=ARCH)
 
+    # Clean un-manipulated snapshot to serve as the Parameter-Space shadow proxy reference
+    shadow_model = copy.deepcopy(reloaded)
+
     if evaluate:
         reloaded.evaluate(
             loader=retain_test_loader, mode="finetuned"
@@ -190,30 +270,41 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu
 
 
 
-    # Define validation tracking steps dynamically
-    evaluation_domains = [
-        {"loader": retain_test_loader, "mode": "retain", "label": "\n--- Performance on Retained Classes"},
-        {"loader": forget_test_loader, "mode": "forget", "label": "\n--- Performance on Forgotten Class"},
-        {"loader": forget_train_loader, "mode": "forget_train", "label": "\n--- Performance on Forgotten Class (Train Set - Verifying Unlearning)"}
-    ]
+    is_retrained = isinstance(strategy, Retrain)
 
-    # Process and append metrics to target reporting paths
-    for domain in evaluation_domains:
-        print(domain["label"])
-        accuracy, report_dict = reloaded.evaluate(loader=domain["loader"], mode=domain["mode"])
-        Util._log_to_csv(
-            arch=ARCH.name,
-            mode=domain["mode"], 
-            accuracy=accuracy, 
-            report_dict=report_dict, 
-            strategy=strategy_in_use
+    if is_retrained:
+        os.makedirs("trained_models", exist_ok=True)
+        reloaded.save(filename=f"class_{forget_class_idx}_retrained.pth")
+
+
+
+    # here we add a condition conditional statement
+    if suite_runner is not None:
+            
+        suite_runner.run_complete_evaluation(
+            framework_name=strategy_in_use,
+            target_class=forget_class_idx,
+            forget_loader=forget_train_loader,
+            retain_test_loader=forget_test_loader,
+            unlearned_instance=reloaded,            
+            base_shadow_instance=shadow_model,  
+            device=device
         )
+    else:
+
+        # Define validation tracking steps dynamically
+        evaluation_domains = [
+            {"loader": retain_test_loader, "mode": "retain", "label": "\n--- Performance on Retained Classes"},
+            {"loader": forget_test_loader, "mode": "forget", "label": "\n--- Performance on Forgotten Class"},
+            {"loader": forget_train_loader, "mode": "forget_train", "label": "\n--- Performance on Forgotten Class (Train Set - Verifying Unlearning)"}
+        ]
+        log_metrics(evaluation_domains, reloaded, strategy_in_use)
 
 
 # entry
 if __name__ == "__main__":
 
-    outer_loop = 10
+    outer_loop = 1
     inner_loop = CLASS_SIZE
     
     for k in range(outer_loop):
@@ -225,7 +316,7 @@ if __name__ == "__main__":
             # Baseline Evaluation 
             # switch finetuning for tests on strategies only, 
             # to avoid finetunning every time we test a strategy 
-            finetuning = True
+            finetuning = False
             run_finetuning_or_baseline_eval(runtime_environment, run_training = finetuning)
             # scale 16400.0 for ResNet
             scale = 20100
@@ -261,13 +352,24 @@ if __name__ == "__main__":
                 arch=ARCH                   
             )
 
+            retrain = Retrain(
+                target_class_index = 0,
+                arch = ARCH,
+                size = CLASS_SIZE,
+                lr = 0.0001,
+                epochs = 14
+
+            )
+
             strategies = [
+                retrain,
+                linear_filtration,
+                weight_filtration,
                 certified_unlearning,
-                #weight_filtration,
-                #linear_filtration
             ]
+            suite_runner = UnlearningAttack(arch=ARCH, class_size=CLASS_SIZE)
                 # Unlearning Iteration
-            for i in range(4, inner_loop):
+            for i in range(inner_loop):
 
                 for strategy in strategies:
 
@@ -282,9 +384,18 @@ if __name__ == "__main__":
                         strategy=strategy,
                         # if we are finetuning, no need to evaluate base model. 
                         # or may be never when not either!
-                        evaluate = not finetuning
+                        evaluate = False,
+                        suite_runner=suite_runner
                     )
+                # just a single class run before running all remaining classes.
+                #print(">> Single check run complete. Verification successful!")
+                #break
+                #dist_attacker.run_adversarial_evaluation()
+                #dist_attacker.run_incremental_evaluation(current_class_step=i)
+
+            if suite_runner is not None:
+                suite_runner.shutdown_hook()
 
         except KeyboardInterrupt:
-            print("program interrupted. Exit!")
+            print("\nprogram interrupted. Exit!")
             break

architectures/Model.py

@@ -108,21 +108,7 @@ class Model(ABC):
         self.model.to(self.device)
         print(f'Model loaded from {file_path}')
 
-    '''
-    def unlearn(self, strategy: 'Strategy', forget_loader, retain_loader):
-        """ Executes a targeted unlearning strategy and profiles efficiency """
-        print(f"Executing: {strategy.__class__.__name__}...")
  
-        start_time = time.time()
-        
-        # Delegate the actual algorithmic weight/logit manipulation to the strategy
-        strategy.apply(self.model, forget_loader, retain_loader)
-        
-        elapsed_time = time.time() - start_time
-        print(f"{strategy.__class__.__name__} completed in {elapsed_time:.4f} seconds.")
-        
-        return elapsed_time
-        '''
 
     def evaluate(self, loader, mode="eval"):
         """

eval/UnlearningAttack.py

@@ -0,0 +1,238 @@
+import torch
+import torch.nn as nn
+import numpy as np
+import os
+from torch.utils.data import DataLoader
+from sklearn.linear_model import LogisticRegression
+from sklearn.metrics import accuracy_score
+from architectures.Model import Model
+
+class UnlearningAttack:
+    def __init__(self, arch, class_size):
+        """
+        Initializes the robust verification suite with universal architecture metadata.
+        Matches Section 5.5 of the thesis text by implementing distinct 
+        Parameter-Space and Logit-Space (Look-alike) attack pipelines uniformly.
+        """
+        self.arch = arch
+        self.class_size = class_size
+        
+        self.hook = None
+        self.model = None  
+        self._hook_features = []
+        self.criterion = nn.CrossEntropyLoss(reduction='none')
+        self.collecting = False
+
+    def _hook_fn(self, module, input, output):
+        if not self.collecting:
+            return
+        flattened_embeddings = torch.flatten(output, 1)
+        self._hook_features.append(flattened_embeddings.detach())
+    
+    def register_model_hook(self, inner_model):
+        if hasattr(inner_model, "original_model"):
+            core_model = inner_model.original_model
+        else:
+            core_model = inner_model
+
+        if hasattr(core_model, 'avgpool'):
+            pool_layer = core_model.avgpool
+        else:
+            pool_layer = None
+            for name, module in core_model.named_modules():
+                if 'pool' in name:
+                    pool_layer = module
+                    break
+
+        if pool_layer is None:
+            raise AttributeError("The target model architecture lacks an 'avgpool' layer block.")
+            
+        self.hook = pool_layer.register_forward_hook(self._hook_fn)
+
+    def shutdown_hook(self):
+        if hasattr(self, 'hook') and self.hook:
+            self.hook.remove()
+            self.hook = None
+
+    def _extract_attack_features(self, target_model, loader, device, target_class):
+        target_model.eval()
+        all_probs = []
+        all_entropies = []
+        all_losses = []
+        self._hook_features = [] 
+        self.collecting = True
+        with torch.no_grad():
+            for data, targets in loader:
+                data, targets = data.to(device), targets.to(device)
+                if target_model.__class__.__name__ == "WF_Module":
+                    gate_signals = torch.full(
+                        (data.size(0),), 
+                        target_class,  
+                        dtype=torch.long, 
+                        device=data.device
+                    )
+                    outputs = target_model(data, target_class_indices=gate_signals)
+                else:
+                    outputs = target_model(data)
+                
+                probs = torch.softmax(outputs, dim=1)
+                all_probs.extend(probs.cpu().numpy())
+                
+                entropy = -torch.sum(probs * torch.log(probs + 1e-10), dim=1)
+                all_entropies.extend(entropy.cpu().numpy())
+                
+                loss = self.criterion(outputs, targets)
+                all_losses.extend(loss.cpu().numpy())
+
+        self.collecting = False
+
+        X_probs = np.array(all_probs)
+        X_entropy = np.array(all_entropies).reshape(-1, 1)
+        X_loss = np.array(all_losses).reshape(-1, 1)
+        X_features = np.hstack([X_probs, X_entropy, X_loss])
+        
+        if self._hook_features:
+            compiled_latent = torch.cat(self._hook_features, dim=0).cpu().numpy()
+        else:
+            compiled_latent = np.zeros((len(X_features), 512))
+
+        return X_features, compiled_latent
+
+    def run_parameter_space_mia(self, unlearned_model, shadow_model, forget_loader, retain_test_loader, device, index):
+        X_shadow_mem, _ = self._extract_attack_features(shadow_model, forget_loader, device, index)
+        X_shadow_non, _ = self._extract_attack_features(shadow_model, retain_test_loader, device, index)
+        
+        min_train = min(len(X_shadow_mem), len(X_shadow_non))
+        X_train = np.vstack([X_shadow_mem[:min_train], X_shadow_non[:min_train]])
+        y_train = np.concatenate([np.ones(min_train), np.zeros(min_train)])
+        
+        attack_classifier = LogisticRegression(max_iter=1000)
+        attack_classifier.fit(X_train, y_train)
+        
+        X_eval_mem, latent_features = self._extract_attack_features(unlearned_model, forget_loader, device, index)
+        X_eval_non, retain_latent = self._extract_attack_features(unlearned_model, retain_test_loader, device, index)
+        
+        min_test = min(len(X_eval_mem), len(X_eval_non))
+        X_test = np.vstack([X_eval_mem[:min_test], X_eval_non[:min_test]])
+        y_test = np.concatenate([np.ones(min_test), np.zeros(min_test)])
+        
+        predictions = attack_classifier.predict(X_test)
+        mia_accuracy = accuracy_score(y_test, predictions)
+        
+        clean_centroid = np.mean(retain_latent, axis=0)
+        forget_distances = np.linalg.norm(latent_features - clean_centroid, axis=1)
+        
+        return mia_accuracy, np.mean(forget_distances)
+
+    def run_logit_space_lookalike_mia(self, filtered_model, naive_retrained, forget_loader, device, target_class):
+        filtered_model.eval()
+        naive_retrained.eval()
+        
+        filtered_logits = []
+        naive_logits = []
+        
+        with torch.no_grad():
+            for data, _ in forget_loader:
+                data = data.to(device)
+                
+                if filtered_model.__class__.__name__ == "WF_Module":
+                    gate_signals = torch.full((data.size(0),), target_class, dtype=torch.long, device=data.device)
+                    out_filtered = filtered_model(data, target_class_indices=gate_signals)
+                else:
+                    out_filtered = filtered_model(data)
+                
+                out_naive = naive_retrained(data)
+                
+                filtered_logits.append(out_filtered)
+                naive_logits.append(out_naive)
+                
+        # Concatenate everything
+        filtered = torch.cat(filtered_logits, dim=0).cpu().numpy()
+        naive = torch.cat(naive_logits, dim=0).cpu().numpy()
+        
+        # Z-Score Normalisation
+        filtered = (filtered - np.mean(filtered, axis=-1, keepdims=True)) / (np.std(filtered, axis=-1, keepdims=True) + 1e-8)
+        naive = (naive - np.mean(naive, axis=-1, keepdims=True)) / (np.std(naive, axis=-1, keepdims=True) + 1e-8)
+        
+        # shuffle indices
+        num_images = len(filtered)
+        image_indices = np.arange(num_images)
+        np.random.shuffle(image_indices)
+
+        # split to train and test set
+        split = int(num_images * 0.7)
+        train_img_idx, test_img_idx = image_indices[:split], image_indices[split:]
+
+        # create a balanced test and train set
+        data_train = np.vstack([filtered[train_img_idx], naive[train_img_idx]])
+        data_test = np.vstack([filtered[test_img_idx], naive[test_img_idx]])
+
+        # labels for attcker (1 from unlearned and 0s to retrained)
+        # we do this because every output retrained gives us is a result of unseen
+        # but unlearned has seen these data.
+        label_train = np.concatenate([np.ones(len(train_img_idx)), np.zeros(len(train_img_idx))])
+        # test set
+        label_test = np.concatenate([np.ones(len(test_img_idx)), np.zeros(len(test_img_idx))])
+
+        
+        adversary = LogisticRegression(max_iter=1000)
+        adversary.fit(data_train, label_train)
+        # evaluate similarity of outputs
+        lookalike_accuracy = accuracy_score(label_test, adversary.predict(data_test))
+        # so that the metric is between 0 and 1.
+        return 2.0 * np.abs(lookalike_accuracy - 0.5)
+
+    def run_complete_evaluation(self, framework_name, target_class, forget_loader, retain_test_loader, unlearned_instance, base_shadow_instance, device):
+        """Orchestrates specific pipeline routing cleanly using cached constructor parameters."""
+        target_dir = os.path.join("reports", framework_name)
+        os.makedirs(target_dir, exist_ok=True)
+        current_log_file = os.path.join(target_dir, "attack_values.csv")
+        
+        if not os.path.exists(current_log_file):
+            with open(current_log_file, "w") as f:
+                f.write("target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy\n")
+
+        self.register_model_hook(unlearned_instance.model)
+
+        # 1. Parameter-Space MIA and Latent Distance
+        parameter_mia_acc, latent_dist = self.run_parameter_space_mia(
+            unlearned_model=unlearned_instance.model,
+            shadow_model=base_shadow_instance.model,
+            forget_loader=forget_loader,
+            retain_test_loader=retain_test_loader,
+            device=device,
+            index=target_class
+        )
+
+        # we load a retrained model to evaluate look_alike tests
+        ghost_checkpoint_path = f"trained_models/class_{target_class}_retrained.pth"
+        
+        if os.path.exists(ghost_checkpoint_path):
+            # Safe clean wrapper boot utilizing internal instance state properties
+            ghost_model_instance = Model.create(arch=self.arch, device=device, size=self.class_size)
+            state_dict = torch.load(ghost_checkpoint_path, map_location=device, weights_only=True)
+            ghost_model_instance.model.load_state_dict(state_dict)
+            reference_model_torch = ghost_model_instance.model
+        else:
+            raise FileNotFoundError(
+                f"Retrained weights not found at: {ghost_checkpoint_path}. \nDid you forget to save models or are they saved with a different path?"
+            )
+
+        lookalike_acc = self.run_logit_space_lookalike_mia(
+            filtered_model=unlearned_instance.model, 
+            naive_retrained=reference_model_torch, 
+            forget_loader=forget_loader, 
+            device=device,
+            target_class=target_class
+        )
+
+        print(f"[{framework_name}] Class {target_class} | Parameter MIA: {parameter_mia_acc:.4f} | Latent Dist: {latent_dist:.4f} | Lookalike: {lookalike_acc:.4f}")
+        
+        with open(current_log_file, "a") as f:
+            f.write(f"{target_class},{parameter_mia_acc:.6f},{latent_dist:.6f},{lookalike_acc:.6f}\n")
+            
+        return {
+            "parameter_mia_accuracy": parameter_mia_acc,
+            "latent_distance": latent_dist,
+            "lookalike_accuracy": lookalike_acc
+        }

reports/CertifiedUnlearning/GOOGLENET/forget.csv

@@ -0,0 +1,3 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/CertifiedUnlearning/GOOGLENET/forget_train.csv

@@ -0,0 +1,3 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/CertifiedUnlearning/GOOGLENET/retain.csv

@@ -0,0 +1,3 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.9171,0.9251,0.9171,0.9180,0.9251,0.9171,0.9180
+0.5539,0.8114,0.5539,0.5527,0.8114,0.5539,0.5527

reports/CertifiedUnlearning/GoogLeNet/time_metrics.txt

@@ -0,0 +1,3 @@
+execution_time_sec
+310.029652
+309.202731

reports/CertifiedUnlearning/RESNET34/forget.csv

@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/CertifiedUnlearning/RESNET34/forget_train.csv

@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0500,1.0000,0.0500,0.0952,1.0000,0.0500,0.0952
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/CertifiedUnlearning/RESNET34/retain.csv

@@ -344,3 +344,23 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.6434,0.8358,0.6434,0.6622,0.8358,0.6434,0.6622
 0.7303,0.8317,0.7303,0.7303,0.8317,0.7303,0.7303
 0.2895,0.4174,0.2895,0.2206,0.4174,0.2895,0.2206
+0.8651,0.8977,0.8651,0.8664,0.8977,0.8651,0.8664
+0.8349,0.8877,0.8349,0.8386,0.8877,0.8349,0.8386
+0.9368,0.9409,0.9368,0.9373,0.9409,0.9368,0.9373
+0.2474,0.6224,0.2474,0.2551,0.6224,0.2474,0.2551
+0.9434,0.9479,0.9434,0.9441,0.9479,0.9434,0.9441
+0.9283,0.9346,0.9283,0.9293,0.9346,0.9283,0.9293
+0.9211,0.9285,0.9211,0.9221,0.9285,0.9211,0.9221
+0.1316,0.5512,0.1316,0.1243,0.5512,0.1316,0.1243
+0.9382,0.9418,0.9382,0.9390,0.9418,0.9382,0.9390
+0.9191,0.9283,0.9191,0.9201,0.9283,0.9191,0.9201
+0.4586,0.7992,0.4586,0.4860,0.7992,0.4586,0.4860
+0.9086,0.9199,0.9086,0.9092,0.9199,0.9086,0.9092
+0.9309,0.9360,0.9309,0.9315,0.9360,0.9309,0.9315
+0.8500,0.9128,0.8500,0.8649,0.9128,0.8500,0.8649
+0.9283,0.9364,0.9283,0.9293,0.9364,0.9283,0.9293
+0.8921,0.9124,0.8921,0.8950,0.9124,0.8921,0.8950
+0.8868,0.9181,0.8868,0.8927,0.9181,0.8868,0.8927
+0.8730,0.9165,0.8730,0.8827,0.9165,0.8730,0.8827
+0.9454,0.9470,0.9454,0.9454,0.9470,0.9454,0.9454
+0.5987,0.7725,0.5987,0.5938,0.7725,0.5987,0.5938

reports/CertifiedUnlearning/ResNet/time_metrics.txt

@@ -358,3 +358,9 @@ execution_time_sec
 395.473056
 395.455440
 395.554517
+395.651279
+400.544514
+395.467196
+395.553217
+395.613947
+395.703417

reports/CertifiedUnlearning/attack_values.csv

@@ -0,0 +1,4 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,7.219862,0.979167
+1,0.500000,3.659238,0.958333
+2,0.500000,6.939345,0.885417

reports/CertifiedUnlearning/attack_values_old.csv

@@ -0,0 +1,101 @@
+target_class,attack_mia_accuracy,latent_distance_tell
+0,0.500000,8.166956
+1,0.500000,6.160398
+2,0.500000,6.704157
+3,0.500000,7.097013
+4,0.500000,7.059480
+5,0.500000,5.941715
+6,0.500000,7.376003
+7,0.500000,6.876045
+8,0.500000,7.853063
+9,0.500000,7.215755
+10,0.500000,6.611487
+11,0.431250,6.596037
+12,0.500000,7.509936
+13,0.500000,6.233299
+14,0.500000,9.069311
+15,0.500000,7.752240
+16,0.500000,7.227110
+17,0.500000,5.331686
+18,0.500000,8.771266
+19,0.500000,5.970541
+0,0.500000,8.333142
+1,0.500000,4.603730
+2,0.500000,6.403101
+3,0.500000,7.975533
+4,0.500000,6.620228
+5,0.500000,8.796431
+6,0.500000,9.078737
+7,0.500000,6.821482
+8,0.500000,9.727625
+9,0.500000,9.074922
+10,0.500000,6.036069
+11,0.493750,7.097591
+12,0.500000,5.960563
+13,0.500000,6.122758
+14,0.500000,8.211535
+15,0.500000,7.850469
+16,0.500000,6.859184
+17,0.500000,5.088897
+18,0.500000,9.236532
+19,0.500000,7.642883
+0,0.500000,8.106592
+1,0.500000,6.134580
+2,0.500000,6.941654
+3,0.500000,7.773781
+4,0.500000,7.363125
+5,0.500000,6.496724
+6,0.500000,7.648515
+7,0.500000,8.689814
+8,0.500000,8.578580
+9,0.500000,9.119745
+10,0.500000,5.984212
+11,0.468750,6.359155
+12,0.500000,7.997709
+13,0.500000,6.927951
+14,0.500000,8.872922
+15,0.500000,7.429983
+16,0.500000,6.928881
+17,0.500000,5.071527
+18,0.500000,8.475766
+19,0.500000,6.096026
+0,0.500000,7.570661
+1,0.500000,3.468966
+2,0.500000,5.726584
+3,0.500000,7.681168
+4,0.500000,7.824241
+5,0.500000,9.169927
+6,0.500000,7.778905
+7,0.500000,8.138535
+8,0.500000,9.735314
+9,0.500000,7.250852
+10,0.500000,6.852473
+11,0.462500,7.474597
+12,0.500000,5.709781
+13,0.500000,7.201421
+14,0.500000,9.513277
+15,0.500000,7.965966
+16,0.500000,6.824328
+17,0.500000,5.539176
+18,0.500000,9.225863
+19,0.500000,7.838114
+0,0.500000,7.209781
+1,0.500000,3.245398
+2,0.500000,6.559930
+3,0.500000,8.221349
+4,0.500000,7.320590
+5,0.500000,8.885569
+6,0.500000,7.456804
+7,0.500000,7.722786
+8,0.500000,8.868426
+9,0.500000,6.132468
+10,0.500000,7.281767
+11,0.481250,7.443795
+12,0.500000,7.379603
+13,0.500000,6.809259
+14,0.500000,8.274336
+15,0.500000,8.232855
+16,0.500000,7.061528
+17,0.500000,6.004038
+18,0.500000,8.139433
+19,0.500000,7.296746

reports/LinearFiltration/RESNET34/forget.csv

@@ -399,3 +399,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/LinearFiltration/RESNET34/forget_train.csv

@@ -399,3 +399,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/LinearFiltration/RESNET34/retain.csv

@@ -399,3 +399,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9572,0.9582,0.9572,0.9573,0.9582,0.9572,0.9573
 0.9599,0.9609,0.9599,0.9599,0.9609,0.9599,0.9599
 0.9579,0.9588,0.9579,0.9580,0.9588,0.9579,0.9580
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
+0.9559,0.9566,0.9559,0.9559,0.9566,0.9559,0.9559
+0.9526,0.9535,0.9526,0.9527,0.9535,0.9526,0.9527
+0.9546,0.9554,0.9546,0.9547,0.9554,0.9546,0.9547
+0.9592,0.9602,0.9592,0.9593,0.9602,0.9592,0.9593
+0.9539,0.9548,0.9539,0.9540,0.9548,0.9539,0.9540
+0.9539,0.9550,0.9539,0.9541,0.9550,0.9539,0.9541
+0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
+0.9539,0.9548,0.9539,0.9540,0.9548,0.9539,0.9540
+0.9539,0.9549,0.9539,0.9541,0.9549,0.9539,0.9541
+0.9539,0.9549,0.9539,0.9540,0.9549,0.9539,0.9540
+0.9513,0.9524,0.9513,0.9514,0.9524,0.9513,0.9514
+0.9520,0.9529,0.9520,0.9520,0.9529,0.9520,0.9520
+0.9572,0.9583,0.9572,0.9574,0.9583,0.9572,0.9574
+0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
+0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
+0.9553,0.9565,0.9553,0.9554,0.9565,0.9553,0.9554
+0.9559,0.9567,0.9559,0.9560,0.9567,0.9559,0.9560
+0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
+0.9559,0.9566,0.9559,0.9559,0.9566,0.9559,0.9559
+0.9526,0.9535,0.9526,0.9527,0.9535,0.9526,0.9527
+0.9546,0.9554,0.9546,0.9547,0.9554,0.9546,0.9547
+0.9592,0.9602,0.9592,0.9593,0.9602,0.9592,0.9593
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9539,0.9548,0.9539,0.9540,0.9548,0.9539,0.9540
+0.9539,0.9550,0.9539,0.9541,0.9550,0.9539,0.9541
+0.9559,0.9568,0.9559,0.9561,0.9568,0.9559,0.9561
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534

reports/LinearFiltration/ResNet/time_metrics.txt

@@ -399,3 +399,192 @@ execution_time_sec
 0.001845
 0.001835
 0.001846
+1.583831
+1.579020
+1.597051
+1.604905
+1.668440
+1.574648
+1.559132
+0.003680
+0.001816
+0.004576
+0.004078
+0.003174
+0.005544
+0.002435
+0.001903
+0.002613
+0.003090
+0.004827
+0.001774
+0.001993
+0.003222
+0.006826
+0.003274
+0.004176
+0.006219
+0.003163
+6.787932
+0.004030
+0.003655
+0.001846
+0.003250
+0.002135
+0.002022
+0.001963
+0.001903
+0.001978
+0.001874
+0.002326
+0.003671
+0.002932
+0.003153
+0.002311
+0.002369
+0.002845
+0.004887
+0.004410
+0.974533
+0.924626
+0.003374
+0.003496
+0.005881
+0.003443
+0.006579
+0.006536
+0.006472
+0.002645
+0.003284
+0.002127
+0.011311
+0.003321
+0.002229
+0.001880
+0.003873
+0.005213
+0.004675
+0.003227
+0.002580
+0.906583
+0.001986
+0.001837
+0.001839
+0.001823
+0.001841
+0.001831
+0.001851
+0.001839
+0.001847
+0.001830
+0.001836
+0.001833
+0.001849
+0.001826
+0.001845
+0.001849
+0.001897
+0.001841
+0.001830
+0.871331
+0.001890
+0.001868
+0.001839
+0.001854
+0.001866
+0.001847
+0.001836
+0.001837
+0.001841
+0.001839
+0.001842
+0.001850
+0.001842
+0.001834
+0.001843
+0.001869
+0.001887
+0.001834
+0.001851
+0.871175
+0.001836
+0.001836
+0.001842
+0.001838
+0.001847
+0.001844
+0.001847
+0.001837
+0.001846
+0.001834
+0.001861
+0.004003
+0.003517
+0.001845
+0.002701
+0.001845
+0.001847
+0.001839
+0.001848
+0.871788
+0.001845
+0.001841
+0.001843
+0.001855
+0.001854
+0.001843
+0.001841
+0.001879
+0.001850
+0.001846
+0.001855
+0.001829
+0.001850
+0.001845
+0.001862
+0.001865
+0.001839
+0.001845
+0.001829
+0.878134
+0.001857
+0.001842
+0.001840
+0.001852
+0.001844
+0.001849
+0.001867
+0.001862
+0.001822
+0.001838
+0.002338
+0.001885
+0.001827
+0.001838
+0.001844
+0.001851
+0.001862
+0.001854
+0.001888
+0.890666
+0.001937
+0.001811
+0.001797
+0.001828
+0.001838
+0.001844
+0.001794
+0.001830
+0.001877
+0.001810
+0.001810
+0.001851
+0.001799
+0.001835
+0.001794
+0.001825
+0.001854
+0.001812
+0.001794
+0.865806
+0.001830

reports/LinearFiltration/attack_values.csv

@@ -0,0 +1,5 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,3.219560,1.000000
+1,0.500000,3.573733,1.000000
+2,0.500000,3.924550,1.000000
+3,0.500000,3.515182,1.000000

reports/LinearFiltration/attack_values_old.csv

@@ -0,0 +1,101 @@
+target_class,attack_mia_accuracy,latent_distance_tell
+0,0.932292,0.000000
+1,0.994792,0.000000
+2,0.411458,0.000000
+3,0.916667,0.000000
+4,0.885417,0.000000
+5,0.968750,0.000000
+6,0.921875,0.000000
+7,0.760417,0.000000
+8,0.437500,0.000000
+9,0.479167,0.000000
+10,0.364583,0.000000
+11,0.458333,0.000000
+12,0.453125,0.000000
+13,0.463542,0.000000
+14,0.708333,0.000000
+15,0.479167,0.000000
+16,0.989583,0.000000
+17,0.937500,0.000000
+18,0.552083,0.000000
+19,0.401042,0.000000
+0,0.942708,0.000000
+1,1.000000,0.000000
+2,0.421875,0.000000
+3,0.932292,0.000000
+4,0.854167,0.000000
+5,0.963542,0.000000
+6,0.958333,0.000000
+7,0.703125,0.000000
+8,0.401042,0.000000
+9,0.479167,0.000000
+10,0.411458,0.000000
+11,0.437500,0.000000
+12,0.489583,0.000000
+13,0.468750,0.000000
+14,0.729167,0.000000
+15,0.484375,0.000000
+16,1.000000,0.000000
+17,0.927083,0.000000
+18,0.510417,0.000000
+19,0.380208,0.000000
+0,0.973958,0.000000
+1,0.994792,0.000000
+2,0.473958,0.000000
+3,0.927083,0.000000
+4,0.911458,0.000000
+5,0.953125,0.000000
+6,0.963542,0.000000
+7,0.697917,0.000000
+8,0.442708,0.000000
+9,0.484375,0.000000
+10,0.416667,0.000000
+11,0.416667,0.000000
+12,0.473958,0.000000
+13,0.494792,0.000000
+14,0.755208,0.000000
+15,0.484375,0.000000
+16,0.994792,0.000000
+17,0.963542,0.000000
+18,0.510417,0.000000
+19,0.395833,0.000000
+0,0.979167,0.000000
+1,1.000000,0.000000
+2,0.390625,0.000000
+3,0.942708,0.000000
+4,0.869792,0.000000
+5,0.979167,0.000000
+6,0.968750,0.000000
+7,0.692708,0.000000
+8,0.437500,0.000000
+9,0.494792,0.000000
+10,0.416667,0.000000
+11,0.416667,0.000000
+12,0.473958,0.000000
+13,0.473958,0.000000
+14,0.640625,0.000000
+15,0.598958,0.000000
+16,0.989583,0.000000
+17,0.968750,0.000000
+18,0.536458,0.000000
+19,0.427083,0.000000
+0,0.984375,0.000000
+1,1.000000,0.000000
+2,0.416667,0.000000
+3,0.927083,0.000000
+4,0.890625,0.000000
+5,0.947917,0.000000
+6,0.973958,0.000000
+7,0.765625,0.000000
+8,0.416667,0.000000
+9,0.515625,0.000000
+10,0.421875,0.000000
+11,0.427083,0.000000
+12,0.458333,0.000000
+13,0.479167,0.000000
+14,0.671875,0.000000
+15,0.494792,0.000000
+16,0.994792,0.000000
+17,0.953125,0.000000
+18,0.500000,0.000000
+19,0.411458,0.000000

reports/Retrain/RESNET34/forget.csv

@@ -0,0 +1,68 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/Retrain/RESNET34/forget_train.csv

@@ -0,0 +1,68 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/Retrain/RESNET34/retain.csv

@@ -0,0 +1,68 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.9618,0.9624,0.9618,0.9619,0.9624,0.9618,0.9619
+0.9625,0.9632,0.9625,0.9626,0.9632,0.9625,0.9626
+0.9572,0.9584,0.9572,0.9575,0.9584,0.9572,0.9575
+0.9566,0.9579,0.9566,0.9568,0.9579,0.9566,0.9568
+0.9658,0.9660,0.9658,0.9658,0.9660,0.9658,0.9658
+0.9651,0.9655,0.9651,0.9651,0.9655,0.9651,0.9651
+0.9539,0.9551,0.9539,0.9540,0.9551,0.9539,0.9540
+0.9632,0.9640,0.9632,0.9632,0.9640,0.9632,0.9632
+0.9618,0.9624,0.9618,0.9620,0.9624,0.9618,0.9620
+0.9612,0.9622,0.9612,0.9613,0.9622,0.9612,0.9613
+0.9592,0.9602,0.9592,0.9593,0.9602,0.9592,0.9593
+0.9566,0.9578,0.9566,0.9567,0.9578,0.9566,0.9567
+0.9605,0.9615,0.9605,0.9605,0.9615,0.9605,0.9605
+0.9539,0.9560,0.9539,0.9542,0.9560,0.9539,0.9542
+0.9632,0.9639,0.9632,0.9632,0.9639,0.9632,0.9632
+0.9625,0.9633,0.9625,0.9625,0.9633,0.9625,0.9625
+0.9625,0.9632,0.9625,0.9626,0.9632,0.9625,0.9626
+0.9625,0.9631,0.9625,0.9626,0.9631,0.9625,0.9626
+0.9592,0.9598,0.9592,0.9591,0.9598,0.9592,0.9591
+0.9586,0.9600,0.9586,0.9588,0.9600,0.9586,0.9588
+0.9566,0.9577,0.9566,0.9567,0.9577,0.9566,0.9567
+0.9605,0.9611,0.9605,0.9605,0.9611,0.9605,0.9605
+0.9612,0.9621,0.9612,0.9613,0.9621,0.9612,0.9613
+0.9625,0.9636,0.9625,0.9626,0.9636,0.9625,0.9626
+0.9625,0.9631,0.9625,0.9624,0.9631,0.9625,0.9624
+0.9553,0.9563,0.9553,0.9554,0.9563,0.9553,0.9554
+0.9566,0.9578,0.9566,0.9567,0.9578,0.9566,0.9567
+0.9632,0.9639,0.9632,0.9632,0.9639,0.9632,0.9632
+0.9592,0.9600,0.9592,0.9593,0.9600,0.9592,0.9593
+0.9553,0.9568,0.9553,0.9554,0.9568,0.9553,0.9554
+0.9612,0.9614,0.9612,0.9611,0.9614,0.9612,0.9611
+0.9625,0.9633,0.9625,0.9626,0.9633,0.9625,0.9626
+0.9618,0.9629,0.9618,0.9620,0.9629,0.9618,0.9620
+0.9553,0.9569,0.9553,0.9554,0.9569,0.9553,0.9554
+0.9513,0.9526,0.9513,0.9515,0.9526,0.9513,0.9515
+0.9612,0.9614,0.9612,0.9612,0.9614,0.9612,0.9612
+0.9572,0.9585,0.9572,0.9573,0.9585,0.9572,0.9573
+0.9599,0.9604,0.9599,0.9598,0.9604,0.9599,0.9598
+0.9592,0.9603,0.9592,0.9594,0.9603,0.9592,0.9594
+0.9599,0.9611,0.9599,0.9600,0.9611,0.9599,0.9600
+0.9632,0.9638,0.9632,0.9632,0.9638,0.9632,0.9632
+0.9599,0.9602,0.9599,0.9599,0.9602,0.9599,0.9599
+0.9526,0.9536,0.9526,0.9527,0.9536,0.9526,0.9527
+0.9579,0.9586,0.9579,0.9579,0.9586,0.9579,0.9579
+0.9579,0.9593,0.9579,0.9580,0.9593,0.9579,0.9580
+0.9599,0.9607,0.9599,0.9600,0.9607,0.9599,0.9600
+0.9632,0.9641,0.9632,0.9633,0.9641,0.9632,0.9633
+0.9599,0.9607,0.9599,0.9601,0.9607,0.9599,0.9601
+0.9664,0.9669,0.9664,0.9665,0.9669,0.9664,0.9665
+0.9592,0.9604,0.9592,0.9594,0.9604,0.9592,0.9594
+0.9625,0.9630,0.9625,0.9625,0.9630,0.9625,0.9625
+0.9579,0.9590,0.9579,0.9581,0.9590,0.9579,0.9581
+0.9572,0.9579,0.9572,0.9573,0.9579,0.9572,0.9573
+0.9632,0.9638,0.9632,0.9632,0.9638,0.9632,0.9632
+0.9566,0.9576,0.9566,0.9566,0.9576,0.9566,0.9566
+0.9651,0.9662,0.9651,0.9653,0.9662,0.9651,0.9653
+0.9579,0.9593,0.9579,0.9581,0.9593,0.9579,0.9581
+0.9592,0.9603,0.9592,0.9593,0.9603,0.9592,0.9593
+0.9671,0.9677,0.9671,0.9672,0.9677,0.9671,0.9672
+0.9638,0.9644,0.9638,0.9639,0.9644,0.9638,0.9639
+0.9645,0.9653,0.9645,0.9646,0.9653,0.9645,0.9646
+0.9618,0.9631,0.9618,0.9620,0.9631,0.9618,0.9620
+0.9599,0.9606,0.9599,0.9599,0.9606,0.9599,0.9599
+0.9553,0.9565,0.9553,0.9553,0.9565,0.9553,0.9553
+0.9533,0.9543,0.9533,0.9534,0.9543,0.9533,0.9534
+0.9586,0.9599,0.9586,0.9588,0.9599,0.9586,0.9588
+0.9612,0.9620,0.9612,0.9613,0.9620,0.9612,0.9613

reports/Retrain/ResNet/time_metrics.txt

@@ -0,0 +1,44 @@
+execution_time_sec
+848.855054
+851.395526
+857.667636
+864.373247
+921.414065
+1006.761514
+851.995606
+850.456523
+851.156817
+855.827699
+852.529868
+855.051966
+851.841468
+852.182889
+859.966127
+870.718984
+859.687153
+849.761404
+892.106300
+880.976200
+894.792684
+918.782255
+862.899020
+848.422644
+848.069965
+849.830024
+850.185797
+850.567450
+850.479165
+849.162948
+850.724711
+848.658417
+850.287266
+848.900766
+849.176482
+849.449771
+850.224029
+848.678724
+851.971777
+850.963888
+848.760931
+848.571131
+856.289965

reports/Retrain/attack_values.csv

@@ -0,0 +1,5 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,11.573492,0.000000
+1,0.500000,12.793120,0.000000
+2,0.500000,12.951434,0.000000
+3,0.500000,10.942259,0.000000

reports/WeightFiltration/RESNET34/forget.csv

@@ -490,3 +490,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1250,1.0000,0.1250,0.2222,1.0000,0.1250,0.2222
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/WeightFiltration/RESNET34/forget_train.csv

@@ -490,3 +490,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.0125,1.0000,0.0125,0.0247,1.0000,0.0125,0.0247
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
 0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0063,1.0000,0.0063,0.0124,1.0000,0.0063,0.0124
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0094,1.0000,0.0094,0.0186,1.0000,0.0094,0.0186
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.1031,1.0000,0.1031,0.1870,1.0000,0.1031,0.1870
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0187,1.0000,0.0187,0.0368,1.0000,0.0187,0.0368
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0031,1.0000,0.0031,0.0062,1.0000,0.0031,0.0062
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
+0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000

reports/WeightFiltration/RESNET34/retain.csv

@@ -490,3 +490,40 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9559,0.9572,0.9559,0.9560,0.9572,0.9559,0.9560
 0.9546,0.9556,0.9546,0.9547,0.9556,0.9546,0.9547
 0.9592,0.9606,0.9592,0.9594,0.9606,0.9592,0.9594
+0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
+0.9572,0.9581,0.9572,0.9573,0.9581,0.9572,0.9573
+0.9546,0.9556,0.9546,0.9546,0.9556,0.9546,0.9546
+0.9526,0.9537,0.9526,0.9527,0.9537,0.9526,0.9527
+0.9539,0.9545,0.9539,0.9540,0.9545,0.9539,0.9540
+0.9566,0.9574,0.9566,0.9566,0.9574,0.9566,0.9566
+0.9533,0.9542,0.9533,0.9534,0.9542,0.9533,0.9534
+0.9539,0.9554,0.9539,0.9541,0.9554,0.9539,0.9541
+0.9533,0.9544,0.9533,0.9534,0.9544,0.9533,0.9534
+0.9533,0.9541,0.9533,0.9534,0.9541,0.9533,0.9534
+0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
+0.9513,0.9525,0.9513,0.9514,0.9525,0.9513,0.9514
+0.9493,0.9509,0.9493,0.9496,0.9509,0.9493,0.9496
+0.9487,0.9500,0.9487,0.9487,0.9500,0.9487,0.9487
+0.9559,0.9575,0.9559,0.9561,0.9575,0.9559,0.9561
+0.9520,0.9531,0.9520,0.9521,0.9531,0.9520,0.9521
+0.9526,0.9536,0.9526,0.9527,0.9536,0.9526,0.9527
+0.9539,0.9557,0.9539,0.9541,0.9557,0.9539,0.9541
+0.9553,0.9559,0.9553,0.9552,0.9559,0.9553,0.9552
+0.9520,0.9530,0.9520,0.9520,0.9530,0.9520,0.9520
+0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
+0.9566,0.9575,0.9566,0.9566,0.9575,0.9566,0.9566
+0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
+0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
+0.9559,0.9569,0.9559,0.9560,0.9569,0.9559,0.9560
+0.9546,0.9555,0.9546,0.9546,0.9555,0.9546,0.9546
+0.9520,0.9530,0.9520,0.9521,0.9530,0.9520,0.9521
+0.9539,0.9545,0.9539,0.9540,0.9545,0.9539,0.9540
+0.9566,0.9574,0.9566,0.9566,0.9574,0.9566,0.9566
+0.9526,0.9541,0.9526,0.9528,0.9541,0.9526,0.9528
+0.9533,0.9542,0.9533,0.9534,0.9542,0.9533,0.9534
+0.9533,0.9547,0.9533,0.9535,0.9547,0.9533,0.9535
+0.9566,0.9575,0.9566,0.9566,0.9575,0.9566,0.9566
+0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
+0.9546,0.9558,0.9546,0.9547,0.9558,0.9546,0.9547
+0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528
+0.9526,0.9540,0.9526,0.9528,0.9540,0.9526,0.9528

reports/WeightFiltration/ResNet/time_metrics.txt

@@ -490,3 +490,167 @@ execution_time_sec
 0.000396
 0.000410
 0.000435
+87.953725
+89.953191
+88.555181
+87.086629
+86.067240
+0.001263
+0.000383
+0.000499
+0.000494
+0.000391
+0.000393
+0.000394
+87.004814
+88.485772
+0.000770
+0.001780
+0.000391
+0.000384
+0.000495
+0.000501
+0.000405
+0.001766
+0.000508
+87.975398
+0.000514
+0.000446
+0.001589
+0.000390
+0.000487
+0.000440
+0.001184
+0.000422
+0.000380
+0.000461
+89.646614
+0.000455
+0.000399
+0.000369
+87.205821
+0.000449
+0.001879
+86.592527
+0.000481
+0.000432
+0.000447
+0.000428
+0.000425
+0.000443
+0.000428
+0.000424
+0.000427
+0.000426
+0.000428
+0.000430
+0.000431
+0.000426
+0.000432
+0.000424
+0.000434
+0.000419
+0.000424
+86.237751
+0.000439
+0.000432
+0.000443
+0.000430
+0.000439
+0.000434
+0.000435
+0.000428
+0.000446
+0.000441
+0.000439
+0.000435
+0.000436
+0.000433
+0.000429
+0.000434
+0.000426
+0.000430
+0.000432
+86.227529
+0.000449
+0.000436
+0.000427
+0.000430
+0.000425
+0.000427
+0.000423
+0.000425
+0.000437
+0.000438
+0.000430
+0.000399
+0.000545
+0.000430
+0.000434
+0.000425
+0.000429
+0.000436
+0.000444
+86.158356
+0.000438
+0.000437
+0.000426
+0.000436
+0.000438
+0.000434
+0.000423
+0.000469
+0.000436
+0.000431
+0.000441
+0.000431
+0.000429
+0.000433
+0.000436
+0.000442
+0.000426
+0.000455
+0.000446
+86.279183
+0.000437
+0.000406
+0.000428
+0.000433
+0.000432
+0.000430
+0.000424
+0.000421
+0.000435
+0.000428
+0.000398
+0.000432
+0.000427
+0.000407
+0.000425
+0.000433
+0.000430
+0.000422
+0.000418
+89.527112
+87.450551
+0.002478
+0.000423
+0.000417
+0.000433
+0.000426
+0.000440
+0.000826
+0.000426
+0.000440
+0.000437
+0.000425
+0.000425
+0.000422
+0.000450
+0.000423
+0.000422
+0.000422
+0.000426
+0.000428
+86.247649
+0.000436

reports/WeightFiltration/attack_values.csv

@@ -0,0 +1,5 @@
+target_class,parameter_mia_accuracy,latent_distance_tell,lookalike_accuracy
+0,0.500000,1.180800,0.958333
+1,0.500000,1.279257,0.968750
+2,0.500000,1.717911,0.937500
+3,0.500000,1.354225,0.989583

reports/WeightFiltration/attack_values_old.csv

@@ -0,0 +1,101 @@
+target_class,attack_mia_accuracy,latent_distance_tell
+0,0.500000,1.107324
+1,0.500000,1.182681
+2,0.500000,1.628934
+3,0.500000,1.260251
+4,0.500000,1.399319
+5,0.500000,2.046023
+6,0.500000,1.750646
+7,0.500000,1.243093
+8,0.500000,1.809917
+9,0.500000,1.702536
+10,0.500000,1.291788
+11,0.500000,1.434109
+12,0.500000,1.448272
+13,0.500000,1.694034
+14,0.500000,1.717611
+15,0.500000,1.758781
+16,0.500000,1.188805
+17,0.500000,1.591978
+18,0.500000,1.445776
+19,0.500000,1.626087
+0,0.500000,1.158666
+1,0.500000,1.245154
+2,0.500000,1.558492
+3,0.500000,1.358110
+4,0.500000,1.339859
+5,0.500000,2.025961
+6,0.500000,1.828531
+7,0.500000,1.186695
+8,0.500000,1.920701
+9,0.500000,1.718948
+10,0.500000,1.374345
+11,0.500000,1.495150
+12,0.500000,1.368306
+13,0.500000,1.710072
+14,0.500000,1.700057
+15,0.500000,1.766020
+16,0.500000,1.160263
+17,0.500000,1.634570
+18,0.500000,1.461136
+19,0.500000,1.697268
+0,0.500000,1.092890
+1,0.500000,1.238615
+2,0.500000,1.704648
+3,0.500000,1.394107
+4,0.500000,1.365094
+5,0.500000,2.032884
+6,0.500000,1.833764
+7,0.500000,1.225851
+8,0.500000,1.807006
+9,0.500000,1.704291
+10,0.500000,1.358446
+11,0.500000,1.555449
+12,0.500000,1.387334
+13,0.500000,1.693131
+14,0.500000,1.736060
+15,0.500000,1.768330
+16,0.500000,1.190044
+17,0.500000,1.585899
+18,0.500000,1.482916
+19,0.500000,1.691146
+0,0.500000,1.141869
+1,0.500000,1.352442
+2,0.500000,1.695588
+3,0.500000,1.432673
+4,0.500000,1.314509
+5,0.500000,2.010463
+6,0.500000,1.817650
+7,0.500000,1.291032
+8,0.500000,1.703021
+9,0.500000,1.802832
+10,0.500000,1.355631
+11,0.500000,1.485411
+12,0.500000,1.441830
+13,0.500000,1.728542
+14,0.500000,1.740982
+15,0.500000,1.764840
+16,0.500000,1.210430
+17,0.500000,1.645152
+18,0.500000,1.471922
+19,0.500000,1.709163
+0,0.500000,1.122816
+1,0.500000,1.332376
+2,0.500000,1.646908
+3,0.500000,1.429030
+4,0.500000,1.321270
+5,0.500000,2.033827
+6,0.500000,1.863828
+7,0.500000,1.242626
+8,0.500000,1.924087
+9,0.500000,1.760985
+10,0.500000,1.423025
+11,0.500000,1.428449
+12,0.500000,1.390632
+13,0.500000,1.619642
+14,0.500000,1.745749
+15,0.500000,1.734899
+16,0.500000,1.144821
+17,0.500000,1.548540
+18,0.500000,1.452088
+19,0.500000,1.721123

reports/base/GOOGLENET/Finetuned.csv

@@ -0,0 +1,2 @@
+accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recall,weighted_f1
+0.9494,0.9504,0.9494,0.9495,0.9504,0.9494,0.9495

reports/base/RESNET34/Finetuned.csv

@@ -51,3 +51,4 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.9519,0.9533,0.9519,0.9520,0.9533,0.9519,0.9520
 0.9581,0.9590,0.9581,0.9581,0.9590,0.9581,0.9581
 0.9513,0.9525,0.9512,0.9514,0.9525,0.9513,0.9514
+0.9531,0.9541,0.9531,0.9532,0.9541,0.9531,0.9532

unlearning/CertifiedUnlearning.py

@@ -44,16 +44,11 @@ class CertifiedUnlearning(Strategy):
         """
         inner_model = getattr(model, "model", model)
    
-        # Check if the current architecture is an Inception variant
-        is_inception = inner_model.__class__.__name__.lower() == "inception3"
-
         params_list = []
         for name, p in inner_model.named_parameters():
             if p.requires_grad:
-                # Discard the disconnected auxiliary training branch weights
-                if is_inception and "AuxLogits" in name:
-                    continue
-                # CRITICAL: Append as a tuple so it can be unpacked as (name, param)
+                
+                # Append as a tuple so it can be unpacked as (name, param)
                 params_list.append((name, p))
                 
         return params_list if named else [e[1] for e in params_list]
@@ -133,7 +128,6 @@ class CertifiedUnlearning(Strategy):
                 
                 h_s = self._hvp(loss, params, h_estimate)
                 
-                # OPTIMIZATION 4: Avoid deprecated .data, use detach() and in-place ops
                 with torch.no_grad():
                     for k in range(len(params)):
                         h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale))

unlearning/LinearFiltration.py

@@ -13,8 +13,8 @@ class LinearFiltration(Strategy):
     def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
         model.eval()
         # Freeze internal params
-        for param in model.parameters():
-            param.requires_grad = False
+        #for param in model.parameters():
+            #param.requires_grad = False
 
         device = next(model.parameters()).device
             
@@ -155,7 +155,8 @@ class LinearFiltration(Strategy):
 
         # 12
         clf = self._get_classifier(model)
-        clf.weight.copy_(W_Z)
+        with torch.no_grad():
+            clf.weight.copy_(W_Z)
 
         return model
     

unlearning/Retrain.py

@@ -1,4 +1,5 @@
-import time
+
+import os
 from pathlib import Path
 import torch
 import torch.nn as nn
@@ -21,27 +22,51 @@ class Retrain(Strategy):
         self.epochs = epochs
 
     def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
-        # 1. Determine the active execution device from the running sandbox
+
         device = next(model.parameters()).device
 
+        # we need to check if a retrained copy exists on disk
+        checkpoint_path = f"trained_models/class_{self.target_class_index}_retrained.pth"
+        if os.path.exists(checkpoint_path):
+            print(f"Found existing retrained model checkpoint at '{checkpoint_path}'. Loading parameters directly...")
+            
+            # Load the state dict using safe configuration flags
+            state_dict = torch.load(checkpoint_path, map_location=device, weights_only=True)
+            
+            # Safely apply the parameter weights to the model in-place
+            model.load_state_dict(state_dict)
+            print("Retrained parameter loading complete (Retraining bypassed).")
+            return model
+
+        # Cache Miss: Execute the standard retraining pipeline
+        print(f"No naive model found for class {self.target_class_index} retraining a new one")
+        
+        
         print(f">> Triggering Exact Unlearning Baseline (Retraining {self.arch.name} from pristine state)...")
+        inner_model = getattr(model, "model", model)
+        if hasattr(inner_model, "fc"):
+            total_classes = inner_model.fc.out_features
+        elif hasattr(inner_model, "classifier"):
+            # Fallback for alternative architecture layout types
+            total_classes = inner_model.classifier[-1].out_features
+        else:
+            total_classes = self.size
         
         # a new model with default params is created
-        fresh_meat = Model.create(self.arch, device, self.size)
+        fresh = Model.create(self.arch, device, total_classes)
         
         # we train it with retain set
-        fresh_meat.train(
+        fresh.train(
             epochs=self.epochs, 
             loader=retain_loader, 
             rate=self.lr, 
             mode="retrain"
         )
         
-        # 4. Extract the trained nn.Module parameter state dict
-        # In-place copy onto the existing sandbox model structure to seamlessly retain downstream evaluations
-        model.load_state_dict(fresh_meat.model.state_dict())
+        # Extract module parameter state dict and copy in place
+        model.load_state_dict(fresh.model.state_dict())
         
-        print(">> Retraining pipeline finished. Pristine baseline weights successfully established.")
+        print("Retraining pipeline complete")
         return model
 
     def _split_data(self, dataset):
@@ -49,5 +74,5 @@ class Retrain(Strategy):
         return get_unlearning_loaders(
             dataset=dataset,
             forget_class_idx=self.target_class_index,
-            batch_size=32
+            batch_size=16
         )

unlearning/Strategy.py

@@ -40,10 +40,12 @@ class Strategy:
         execution_time = end_time - start_time
         
         # Log to the strategy's specific file
+        '''
         Util.log_metric(
             log_file=log_file, 
             execution_time=execution_time
         )
+        '''
 
         print(f"[{self.strategy_name}] Completed in {execution_time:.6f} seconds. Saved to {log_file}")
         

unlearning/WeightFiltration.py

@@ -114,7 +114,7 @@ class WeightFiltration(Strategy):
         model.eval()
         
         if self.wf_model is None:
-            print(">> Initializing and compiling global WF-Net matrix (Run Once for all classes)...")
+            print("Initializing and compiling global WF-Net matrix (Run Once for all classes)...")
             
             self.wf_model = self._optimise_filter(
                 model, 
@@ -123,10 +123,10 @@ class WeightFiltration(Strategy):
                 device=device
             )
         else:
-            print(f">> Gating matrix loaded. Switching layout to target class index: {self.target_class_index}")
+            print(f"Gating matrix loaded. Switching layout to target class index: {self.target_class_index}")
             self.wf_model.target_class_index = self.target_class_index
             
-        return self.wf_model
+        return self.wf_model.get()
     
     def _split_data(self, dataset):
         return vertical_split(