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"
@@ -188,32 +268,43 @@ def run_unlearning_and_strategy_eval(env_dict, forget_class_idx, strategy, evalu
     else:
         reloaded = unlearned
 
-    
 
-    # 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)"}
-    ]
 
-    # 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
+    is_retrained = isinstance(strategy, Retrain)
+
+    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
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reports/CertifiedUnlearning/RESNET34/retain.csv

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reports/CertifiedUnlearning/ResNet/time_metrics.txt

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reports/CertifiedUnlearning/attack_values.csv

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reports/LinearFiltration/RESNET34/forget.csv

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reports/LinearFiltration/RESNET34/forget_train.csv

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reports/LinearFiltration/RESNET34/retain.csv

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reports/Retrain/attack_values.csv

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reports/WeightFiltration/RESNET34/forget.csv

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reports/WeightFiltration/RESNET34/forget_train.csv

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reports/WeightFiltration/RESNET34/retain.csv

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reports/base/GOOGLENET/Finetuned.csv

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+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

@@ -43,17 +43,12 @@ class CertifiedUnlearning(Strategy):
         InceptionV3 auxiliary layers and tracking gradients.
         """
         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]
@@ -92,7 +87,7 @@ class CertifiedUnlearning(Strategy):
         first_grads = grad(loss, params, retain_graph=True, create_graph=True)
         elemwise_products = sum(torch.sum(g_elem * v_elem) for g_elem, v_elem in zip(first_grads, v))
         return grad(elemwise_products, params, create_graph=False)
-
+    
     def _stochastic_newton_update(self, g, dataset, model, device):
         model.eval()
         criterion = nn.CrossEntropyLoss()
@@ -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))
@@ -143,7 +137,7 @@ class CertifiedUnlearning(Strategy):
                 if global_step % step_interval == 0 and current_pct < 100:
                     current_pct += 1
                     print(f"\rProgress: {current_pct}% done", end="", flush=True)
-                        
+                    
             with torch.no_grad():
                 for k in range(len(params)):
                     h_res[k] += h_estimate[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(