cleaned up for submission

Note.md

@@ -1,40 +0,0 @@
-Not at all! You are still completely faithful to Guo et al. Your current implementation does **not** break their framework.
-
-In fact, your setup matches the exact methodology of the paper. There is a common misconception about what Guo et al. mean when they caution against calculating the Hessian inverse, and understanding how your feature extraction relates to their theory explains why your code remains completely valid.
-
----
-
-## 1. What Guo et al. *Actually* Said
-
-In the original paper (*"Certified Removal from Linear Models"*), Guo et al. state that explicitly calculating and inverting the Hessian matrix becomes prohibitively expensive when the parameter count $d$ scales up.
-
-$$\text{Time Complexity to Invert } H = O(d^3)$$
-
-However, the authors explicitly implemented and verified their approach on **linear classifiers** (like logistic regression) where the input feature dimension $d$ was small enough to handle directly.
-
-When you strip out the heavy ResNet50 convolutional layers and turn the backbone into a static feature extractor, **you transform your deep network into a linear classifier.** ```
-[Images] ──> [Frozen ResNet Backbone] ──> Extracted Feature Vector (d = 2048) ──> [Linear Head (model.fc)]
-
-
-Because your feature vector is exactly 2,048 dimensions, your Hessian matrix is a modest $2048 \times 2048$. 
-
-Inverting a $2048 \times 2048$ matrix takes your CPU less than **0.5 seconds** ($2048^3$ operations is tiny for a modern processor). You are executing the exact mathematics Guo et al. prescribed for linear systems. You haven't broken their implementation; you have successfully reduced a non-convex deep learning problem into their exact convex linear domain.
-
-
-## 2. Where Hessian-Free Approximations (Like LiSSA) Apply
-
-The reason alternative methods like LiSSA or Conjugate Gradient exist is for scenarios where you *cannot* reduce the model to a small linear head.
-
-If you decided to apply Certified Removal to the **entire ResNet50 network** (all 23.5 million parameters open), then you would be forced to abandon your exact matrix calculation. Inverting a $23.5\text{M} \times 23.5\text{M}$ matrix is impossible. In that specific scenario, you would have to use a Hessian-free approximation method to avoid breaking the budget.
-
----
-
-## 3. The Core Alignment with the Paper
-
-Your script implements the three pillars that define Guo et al.’s Certified Removal:
-
-1. **The Optimization Target:** It uses an $L_2$-regularized objective function (`self.l2_reg`).
-2. **The Newton Step:** It takes the exact second-order curvature correction ($H^{-1} \nabla$) to adjust the parameters.
-3. **The Indistinguishability Guarantee:** It applies a privacy perturbation boundary check (`self.removal_bound`).
-
-Your implementation is an elegant, academically sound adaptation of their linear model theory for a deep learning architecture. By handling the feature extraction step first, you made their exact algorithm run efficiently within a 4GB VRAM envelope.

Predict.py

@@ -1,67 +0,0 @@
-
-
-
-import torch
-import numpy as np
-
-@torch.inference_mode() # More memory-efficient than no_grad()
-def get_loss_per_sample(model, data_loader, device):
-    """
-    Returns a list of individual losses for every sample in the loader.
-    Useful for MIA to see how 'certain' the model is about specific images.
-    """
-    model.eval()
-    criterion = torch.nn.CrossEntropyLoss(reduction='none') # Crucial: returns loss per image
-    all_losses = []
-
-    for inputs, labels in data_loader:
-        inputs, labels = inputs.to(device), labels.to(device)
-        
-        outputs = model(inputs)
-        
-        # Calculate loss for each image in the batch individually
-        loss = criterion(outputs, labels)
-        
-        all_losses.extend(loss.cpu().numpy())
-
-    return all_losses
-
-
-@torch.inference_mode()
-def get_losses_by_class(model, data_loader, device):
-    """
-    Returns a dictionary: { class_id: [list_of_losses_for_this_class] }
-    """
-    model.eval()
-    criterion = torch.nn.CrossEntropyLoss(reduction='none')
-    
-    class_losses = {}
-
-    for inputs, labels in data_loader:
-        inputs, labels = inputs.to(device), labels.to(device)
-        outputs = model(inputs)
-        
-        # Get individual losses
-        losses = criterion(outputs, labels).cpu().numpy()
-        labels_np = labels.cpu().numpy()
-
-        for i, class_id in enumerate(labels_np):
-            if class_id not in class_losses:
-                class_losses[class_id] = []
-            class_losses[class_id].append(losses[i])
-
-    return class_losses
-
-
-# evaluate MIA
-def eval_MIA(forgotten_losses, never_seen_losses):
-    avg_f_loss = np.mean(forgotten_losses)
-    avg_ns_loss = np.mean(never_seen_losses)
-
-    print(f"Average Loss on Forgotten Identity: {avg_f_loss:.4f}")
-    print(f"Average Loss on Unknown Identities: {avg_ns_loss:.4f}")
-
-    if avg_f_loss < avg_ns_loss * 0.8:
-        print("MIA Warning: Model still shows high certainty on forgotten data.")
-    else:
-        print("MIA Success: Model treats forgotten data like unknown data.")

SetUp.py

@@ -1,14 +0,0 @@
-##
-import torch
-from torchvision import datasets, transforms, models
-
-def get_device():
-
-    if torch.cuda.is_available():
-        # clear cach to boost memory
-        # for new round
-        torch.cuda.empty_cache()
-        return torch.device("cuda")
-    else:
-        return torch.device("cpu")
-

Tune.py

@@ -5,7 +5,6 @@ from sklearn.metrics import classification_report
 import copy
 
 # Framework and Utility Imports
-import SetUp
 import Util
 from sets.Data import *
 from sets.IdentitySubset import IdentitySubset
@@ -43,6 +42,16 @@ RESOLUTION:int = 224
 '''
 ARCH = Architecture.RESNET34
 
+def get_device():
+
+    if torch.cuda.is_available():
+        # clear cach to boost memory
+        # for new round
+        torch.cuda.empty_cache()
+        return torch.device("cuda")
+    else:
+        return torch.device("cpu")
+
 
 # Data preparation and model setup
 def prepare_data_and_model_environment():
@@ -50,7 +59,8 @@ def prepare_data_and_model_environment():
     Handles environment discovery, downloads/loads datasets, generates 
     train-test class splits, and configures the architecture base.
     """
-    device = SetUp.get_device()
+    # get Cuda or CPU.
+    device = get_device()
     dataset_name = Set_Name.CASIAFACES
     if dataset_name == Set_Name.CASIAFACES:
         SAMPLE_SIZE = 400

Util.py

@@ -2,6 +2,7 @@
 from pathlib import Path
 import os
 
+# used for logging metrics to files
 def _log_to_csv(arch, mode, accuracy, report_dict, strategy):
         """Handles directory structures, file setups, and distinct CSV column formatting."""
         #arch_name = model.__class__.__name__.lower()
@@ -33,7 +34,7 @@ def _log_to_csv(arch, mode, accuracy, report_dict, strategy):
             
         print(f">> Direct CSV metrics appended to {csv_path}")
 
-
+# creates a file for 
 def _initialize_log_file(log_file):
     """Creates a unique log file for this strategy with a header if it doesn't exist."""
     log_file.parent.mkdir(parents=True, exist_ok=True)

architectures/EfficentNet.py

@@ -11,8 +11,8 @@ class EfficientNet(Model):
 
         m = models.efficientnet_b1(weights=models.EfficientNet_B1_Weights.DEFAULT)
 
-        # Unfreeze the last block for a lighter touch
+        # Unfreeze the last block 
-        for param in m.features[-1].parameters(): param.requires_grad = True
+       # for param in m.features[-1].parameters(): param.requires_grad = True
 
         # Standard classifier fix
         m.classifier[1] = nn.Linear(m.classifier[1].in_features, self.size)

architectures/Model.py

@@ -31,13 +31,13 @@ class Model(ABC):
         optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), lr=rate, weight_decay=0.1)
         
         scheduler = CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-6)
-        # to save reports
+        # to save training time to reports
-        # file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
+        file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
-        # Util._initialize_log_file(file_path)
+        Util._initialize_log_file(file_path)
         
 
         print(f"Starting training on {self.device}...")
-        # start_time = time.time()
+        start_time = time.time()
         # training phase
         self.model.train()
 
@@ -49,7 +49,7 @@ class Model(ABC):
                 optimizer.zero_grad()
                 # forward pass
                 outputs = self.model(inputs)
-                # comoutew loss
+                # comopute loss
                 loss = criterion(outputs, labels)
                 # backward pass
                 loss.backward()
@@ -58,9 +58,9 @@ class Model(ABC):
             scheduler.step()
         
             print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}")
-        #end_time = time.time()
+        end_time = time.time()
-        #execution_time = end_time - start_time
+        execution_time = end_time - start_time
-        #Util.log_metric(log_file=file_path, execution_time=execution_time)
+        Util.log_metric(log_file=file_path, execution_time=execution_time)
         if self.device.type == 'cuda': torch.cuda.synchronize()
         print(f"Training complete.")
 
@@ -123,10 +123,10 @@ class Model(ABC):
         
         print(f"Test Accuracy: {accuracy:.2f}%")
         
-        # 1. Print standard text report to terminal
+        # Print standard text report to terminal
-        print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
+        #print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
         
-        # 2. Extract structured dictionary metrics
+        # Structured dictionary metrics
         report_dict = classification_report(
             all_labels, 
             all_preds, 
@@ -135,8 +135,7 @@ class Model(ABC):
             zero_division=0
         )
         
-        # 3. Delegate file tracking to isolated helper method
+        # report metrics to choriographer
-        #self._log_to_csv(mode, accuracy,report_dict)
         return accuracy, report_dict
 
 

architectures/WFNet.py

@@ -6,69 +6,6 @@ from torch.utils.data import DataLoader
 import numpy as np
 from sklearn.metrics import classification_report
 from architectures.Model import Model
-
-'''class WF_Module(nn.Module):
-    """
-    Pure PyTorch Neural Network module graph.
-    Keeps parameter registration and autograd tracking separate from 
-    the framework's high-level Model abstractions to prevent recursion collisions.
-    """
-    def __init__(self, original_model: nn.Module, num_classes: int):
-        super().__init__()
-    
-        self.original_model = original_model
-        
-        # Target layer for weight filtering (layer4 block 1 conv2 or conv3 depending on arch)
-        last_layer = original_model.layer4[1]
-
-        # Some versions are limited to 2 convolutional layers
-        if hasattr(last_layer, "conv3"):
-            self.target_conv = last_layer.conv3
-        else:
-            self.target_conv = last_layer.conv2
-
-        # Completely freeze the original ResNet parameters
-        for param in self.parameters():
-            param.requires_grad = False
-            
-        # Initialize the alpha parameter matrix (Rows = Classes, Cols = Channels)
-        out_channels = self.target_conv.weight.shape[0]
-        self.alpha = nn.Parameter(torch.full((num_classes, out_channels), 3.0))'''
-
-'''
-    Poppi et_al's Single-shot multiclass unlearning.
-    This calculation happens only once to generate the mask. once the mask is generated,
-    Unlearning and remembering becomes a matter of switching gates on and off.'''
-'''
-    def forward(self, x: torch.Tensor, target_class_indices: torch.Tensor) -> torch.Tensor:
-        # we linearly loop through layers 1 to 4[block 1] (for ResNet) 
-        # for i in M_{|L|} do  l <- l[i]
-        x = self.original_model.maxpool(self.original_model.relu(self.original_model.bn1(self.original_model.conv1(x))))
-        x = self.original_model.layer1(x)
-        x = self.original_model.layer2(x)
-        x = self.original_model.layer3(x)
-        x = self.original_model.layer4[0](x)
-        
-        # The second block execute its internal transformations natively
-        # This handles conv1->conv2 (ResNet18) or conv1->conv2->conv3 (ResNet50) automatically!
-        # Xi+1 <- l(Xi, ˆwl)
-        x = self.original_model.layer4[1](x)
-        
-        # Apply mask dynamically to the completed block feature map
-        # wl <- αl[Yunl] ⊙ ˆwl
-        batch_alpha = self.alpha[target_class_indices]
-        mask = torch.sigmoid(batch_alpha).view(x.size(0), -1, 1, 1)
-        x = x * mask
-        
-        # Remaining standard head steps
-        x = self.original_model.avgpool(x)
-        x = torch.flatten(x, 1)
-        # so here we are returning the output logits
-        # the result of classification is then 
-        # argmax(x)
-        return self.original_model.fc(x)
-'''
-
 class WF_Module(nn.Module):
     def __init__(self, original_model: nn.Module, num_classes: int, arch_enum):
         super().__init__()

eval/UnlearningAttack.py

@@ -32,7 +32,8 @@ class UnlearningAttack:
                 if model1.__class__.__name__ == "WF_Module":
                     gate = torch.full((data.size(0),), target_class, device=device)
                     p1 = torch.softmax(model1(data, target_class_indices=gate), dim=1)
-                else:
+                    
+                else: # its a normal nn.Module
                     p1 = torch.softmax(model1(data), dim=1)
                 p2 = torch.softmax(model2(data), dim=1)
                 probs1.extend(p1.cpu().numpy()); probs2.extend(p2.cpu().numpy())

js_evaluator/JS_Evaluator.py

@@ -1,111 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch.utils.data import DataLoader
-import torchvision.models as models
-
-class ZeroRetrainForgettingEvaluator:
-    def __init__(self, unlearned_model: nn.Module, num_classes: int):
-        """
-        Initializes the ZRF Evaluator.
-        
-        Args:
-            unlearned_model (nn.Module): Your fine-tuned & unlearned ResNet-50.
-            num_classes (int): Number of classes used in your CelebA task.
-        """
-        # select device
-        if torch.cuda.is_available():
-            self.device = torch.device("cuda")
-        elif hasattr(torch, "xpu") and torch.xpu.is_available():
-            self.device = torch.device("xpu") # For Intel GPUs using IPEX
-        else:
-            self.device = torch.device("cpu")
-            
-        print(f"[INFO] Using device: {self.device}")
-
-        # prepare the unlearned model
-        self.unlearned_model = unlearned_model.to(self.device)
-        self.unlearned_model.eval()
-
-        # Instantiate a structurally matching, completely random model
-        print(f"[INFO] Initializing random baseline ResNet-50 with {num_classes} classes...")
-        self.random_model = self.get_random_model(num_classes)
-        self.random_model = self.random_model.to(self.device)
-        self.random_model.eval()
-
-   # gets randomly initialised model 
-   # for comparison with unlearned model
-    def get_random_model(num_classes):
-        print(f"[INFO] Initializing random baseline ResNet-50 with {num_classes} classes...")
-        model = models.resnet50(weights=None)
-        model.fc = nn.Linear(model.fc.in_features, num_classes)
-        return model
-
-
-    # compute divergence
-    def _compute_js_divergence(self, p: torch.Tensor, q: torch.Tensor) -> float:
-        """
-        Computes the Jensen-Shannon (JS) Divergence between two probability distributions.
-        
-        Args:
-            p, q (Tensor): Tensors of shape (batch_size, num_classes) containing probabilities.
-        """
-        # Avoid log(0) issues by adding a tiny epsilon
-        eps = 1e-12
-        p = torch.clamp(p, eps, 1.0)
-        q = torch.clamp(q, eps, 1.0)
-        
-        # Calculate the midpoint distribution
-        m = 0.5 * (p + q)
-        
-        # Compute KL Divergence natively: KL(P || M) and KL(Q || M)
-        kl_pm = torch.sum(p * (torch.log(p) - torch.log(m)), dim=1)
-        kl_qm = torch.sum(q * (torch.log(q) - torch.log(m)), dim=1)
-        
-        # JS Divergence is the average of both KL divergences
-        js_div = 0.5 * (kl_pm + kl_qm)
-        
-        # Return the mean divergence across the entire batch
-        return js_div.mean().item()
-
-    def evaluate_forget_class(self, dataset, batch_size: int = 32) -> float:
-        """
-        Evaluates the unlearned model against the random model using images 
-        from the forgotten class/identity.
-        
-        Args:
-            dataset (Dataset): A PyTorch Dataset containing images of the forget set.
-            batch_size (int): Batch size for evaluation.
-            
-        Returns:
-            float: The ZRF score (JS Divergence). A lower divergence means 
-                   the unlearned model is behaving exactly like a random model.
-        """
-        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=False)
-        
-        total_js_div = 0.0
-        total_samples = 0
-
-        # No gradients needed for evaluation
-        with torch.no_grad():
-            for images, _ in dataloader:
-                images = images.to(self.device)
-                batch_len = images.size(0)
-
-                # Get raw outputs (logits)
-                unlearned_logits = self.unlearned_model(images)
-                random_logits = self.random_model(images)
-
-                # Convert logits to probability distributions via Softmax
-                unlearned_probs = F.softmax(unlearned_logits, dim=1)
-                random_probs = F.softmax(random_logits, dim=1)
-
-                # Calculate JS divergence for this batch
-                batch_js = self._compute_js_divergence(unlearned_probs, random_probs)
-                
-                # Weighted average based on batch size (handles final smaller batches perfectly)
-                total_js_div += batch_js * batch_len
-                total_samples += batch_len
-
-        final_zrf_score = total_js_div / total_samples
-        return final_zrf_score

reports/CertifiedUnlearning/RESNET34/forget.csv

@@ -471,32 +471,3 @@ 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
-0.1500,1.0000,0.1500,0.2609,1.0000,0.1500,0.2609
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-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
-0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.1125,1.0000,0.1125,0.2022,1.0000,0.1125,0.2022

reports/CertifiedUnlearning/RESNET34/forget_train.csv

@@ -471,32 +471,3 @@ 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
-0.0250,1.0000,0.0250,0.0488,1.0000,0.0250,0.0488
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-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
-0.0375,1.0000,0.0375,0.0723,1.0000,0.0375,0.0723
-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
-0.0656,1.0000,0.0656,0.1232,1.0000,0.0656,0.1232
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-0.0000,0.0000,0.0000,0.0000,0.0000,0.0000,0.0000
-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
-0.0875,1.0000,0.0875,0.1609,1.0000,0.0875,0.1609

reports/CertifiedUnlearning/RESNET34/retain.csv

@@ -471,32 +471,3 @@ accuracy,macro_precision,macro_recall,macro_f1,weighted_precision,weighted_recal
 0.1026,0.1456,0.1026,0.0392,0.1456,0.1026,0.0392
 0.6928,0.8887,0.6928,0.7218,0.8887,0.6928,0.7218
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reports/CertifiedUnlearning/attack_values.csv

@@ -19,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
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@@ -103,5 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
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reports/LinearFiltration/RESNET34/full-set/forget.csv

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reports/LinearFiltration/RESNET34/full-set/forget_train.csv

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reports/LinearFiltration/RESNET34/full-set/retain.csv

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reports/LinearFiltration/RESNET34_old/forget.csv

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reports/LinearFiltration/ResNet/time_metrics.txt

@@ -39,17 +39,6 @@ execution_time_sec
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reports/LinearFiltration/ResNet_old/Report sample.txt

@@ -1,3 +0,0 @@
-on full data:
-
-- average first run = 16.495034

reports/LinearFiltration/ResNet_old/old.txt

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reports/LinearFiltration/attack_values_old.csv

@@ -1,108 +0,0 @@
-target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
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reports/Retrain/attack_values.csv

@@ -1,7 +1,4 @@
 target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
-0,0.500000,0.000000, 0.156250, 0.000000
-1,0.500000,0.000000, 0.104167, 0.000000
-2,0.500000,0.000000, 0.114583, 0.000000
 0,0.500000,0.000000, 0.125000, 0.000000
 1,0.500000,0.000000, 0.072917, 0.000000
 2,0.500000,0.000000, 0.020833, 0.000000
@@ -22,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
 17,0.500000,0.000000, 0.093750, 0.000000
 18,0.531250,0.000000, 0.010417, 0.000000
 19,0.500000,0.000000, 0.031250, 0.000000
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@@ -106,8 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
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reports/Retrain/attack_values_int.csv

@@ -1,38 +0,0 @@
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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/WeightFiltration/ResNet/time_metrics.txt

@@ -299,17 +299,6 @@ execution_time_sec
 0.000427
 0.000398
 0.000403
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 177.630187
 0.000413
 0.000469
@@ -450,207 +439,3 @@ execution_time_sec
 0.000431
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 0.000410
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reports/WeightFiltration/attack_values.csv

@@ -19,10 +19,6 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
 17,0.500000,0.927083, 0.114583, 0.536984
 18,0.500000,0.947917, 0.072917, 0.529271
 19,0.500000,0.958333, 0.062500, 0.552560
-0,0.500000,0.968750, 0.208333, 0.536183
-1,0.500000,0.968750, 0.125000, 0.538881
-2,0.500000,0.958333, 0.031250, 0.567163
-3,0.500000,0.979167, 0.114583, 0.562516
 0,0.500000,0.952083, 0.000000, 0.537566
 1,0.500000,0.912500, 0.083333, 0.535581
 2,0.500000,0.958333, 0.010417, 0.564744
@@ -103,6 +99,3 @@ target_class, parameter_mia_accuracy, lookalike_accuracy, A-Dist, JS-Dist
 17,0.500000,0.937500, 0.093750, 0.532926
 18,0.500000,0.937500, 0.031250, 0.532182
 19,0.500000,0.962500, 0.041667, 0.552156
-0,0.500000,0.960417, 0.031250, 0.534253
-1,0.500000,0.910417, 0.000000, 0.534778
-2,0.500000,0.922917, 0.000000, 0.560311

reports/WeightFiltration/attack_values_old.csv

@@ -1,101 +0,0 @@
-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
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-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

retrain/resnet34/time_metrics.txt

@@ -1,9 +0,0 @@
-execution_time_sec
-991.073163
-978.072894
-982.032442
-1081.642368
-985.512192
-979.091341
-978.852345
-978.963139

unlearning/CertifiedUnlearning.py

@@ -7,7 +7,6 @@ from unlearning.Strategy import Strategy
 from sets.Data import *
 
 # Single-Batch Certified Unlearning for DNNs
-
 class CertifiedUnlearning(Strategy):
     """
     Implements Certified Unlearning for non-convex DNNs (Zhang et al.).
@@ -47,8 +46,7 @@ class CertifiedUnlearning(Strategy):
         params_list = []
         for name, p in inner_model.named_parameters():
             if p.requires_grad:
-                
+                # 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 +131,7 @@ class CertifiedUnlearning(Strategy):
                         h_estimate[k].copy_(h_estimate[k] + g[k] - (h_s[k] / self.scale))
                         
 
-                # feed back on status
+                # feed back because this took agaes and it was not clear if things were working
                 if global_step % step_interval == 0 and current_pct < 100:
                     current_pct += 1
                     print(f"\rProgress: {current_pct}% done", end="", flush=True)
@@ -167,7 +165,7 @@ class CertifiedUnlearning(Strategy):
                         # Keep early low-level vision filters entirely pristine
                         pass
                 
-                # Move to the next calculated Hessian vector block only after a valid update step
+                # to the next calculated Hessian vector block
                 delta_idx += 1
                 
         return model

unlearning/LinearFiltration.py

@@ -124,7 +124,6 @@ class LinearFiltration(Strategy):
         if self.A is None:
             self._compute_A(
                 model = model,
-                #num_classes = num_classes,
                 loader = forget_loader,
                 device = device
             )
@@ -147,6 +146,9 @@ class LinearFiltration(Strategy):
         A_inv = torch.linalg.pinv(A_t, rcond=1e-2)
         # 11 
         W_temp = B_z @ A_inv @ W 
+        # Linear (Normalisation)filtration is done here, 
+        # next steps are added for safe automated evaluations
+
         # with one class removed, we have a  head W_temp for 19 classes.
         # but we have loaders for 20 classes for evaluation. so ,
         # map K-1  W back to K x K.
@@ -164,6 +166,7 @@ class LinearFiltration(Strategy):
             # neutralise forget bias if exists
             if clf.bias is not None:
                 n_bias = clf.bias.data.clone()
+                # push it down to negative
                 n_bias[forget_index] = -1e9
                 clf.bias.copy_(n_bias)
 

unlearning/Retrain.py

@@ -42,7 +42,7 @@ class Retrain(Strategy):
         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)...")
+        print(f"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

unlearning/WF.py

@@ -1,107 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from torch.utils.data import DataLoader
-from unlearning.Strategy import Strategy
-from .wf.WF_Net import WF_Net
-
-class WeightF(Strategy):
-    """
-    Verbatim implementation of Poppi et al.'s WF-Net framework modified
-    for static, single-class unlearning extraction.
-    """
-    def __init__(self, target_class_index: int, epochs: int = 10, lr: float = 0.2, gamma: float = 10.0):
-        super().__init__(target_class_index=target_class_index)
-        self.epochs = epochs
-        self.lr = lr
-        self.gamma = gamma 
-
-    def _optimise_filter(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader, device):
-        num_classes = model.fc.out_features
-        wf_model = WF_Net(original_model=model, num_classes=num_classes).to(device)
-                
-        # Optimize only the specific alpha masks
-        optimizer = optim.Adam([wf_model.alpha], lr=self.lr)
-        criterion = nn.CrossEntropyLoss() # Default reduction is 'mean'
-        
-        for epoch in range(self.epochs):
-            forget_iter = iter(forget_loader)
-            t_loss_r, t_loss_f = 0.0, 0.0
-            steps = 0
-                        
-            for r_inputs, r_labels in retain_loader:
-                r_inputs, r_labels = r_inputs.to(device), r_labels.to(device)
-                                
-                try:
-                    f_inputs, _ = next(forget_iter)
-                except StopIteration:
-                    forget_iter = iter(forget_loader)
-                    f_inputs, _ = next(forget_iter)
-                f_inputs = f_inputs.to(device)
-                                
-                optimizer.zero_grad()
-                                
-                # Forward Pass
-                outputs_r = wf_model(r_inputs, target_unlearn_class=self.target_class_index)
-                outputs_f = wf_model(f_inputs, target_unlearn_class=self.target_class_index)
-                                
-                # Retain Loss (Mean over batch)
-                loss_r = criterion(outputs_r, r_labels)
-                                
-                # Forget Loss (Corrected to Mean over batch)
-                temperature = 1.0
-                logits_f_scaled = outputs_f / temperature
-                
-                # Compute uniform target entropy per-sample, then average over the batch
-                log_probs_f = torch.log_softmax(logits_f_scaled, dim=-1)
-                uniform_target = torch.ones_like(logits_f_scaled) / num_classes
-                loss_f = -torch.sum(uniform_target * log_probs_f, dim=-1).mean()
-                                
-                total_loss = loss_r + (self.gamma * loss_f)
-                total_loss.backward()
-                optimizer.step()
-                                
-                t_loss_r += loss_r.item()
-                t_loss_f += loss_f.item()
-                steps += 1
-                            
-            print(f"  Epoch {epoch+1}/{self.epochs} | Retain Loss: {t_loss_r/steps:.4f} | Forget Loss: {t_loss_f/steps:.4f}")
-        return wf_model
-                
-    def _run(self, model: nn.Module, forget_loader: DataLoader, retain_loader: DataLoader) -> nn.Module:
-        device = next(model.parameters()).device
-        model.eval()                 
-
-        if hasattr(model, 'layer4') and len(model.layer4) > 1:
-            target_conv = model.layer4[1].conv2
-        else:
-            raise AttributeError("Model architecture does not match expected ResNet-18 structure.")
-                    
-        original_weights = target_conv.weight.data.clone().detach()
-        out_channels = original_weights.shape[0]
-        
-        # Freeze global network layers
-        for p in model.parameters():
-            p.requires_grad = False
-            
-        wf_model = self._optimise_filter(
-            model,
-            forget_loader=forget_loader,
-            retain_loader=retain_loader,
-            device=device,
-        )
-        
-        # --- PERMANENT BAKING STEP ---
-        with torch.no_grad():
-            # Grab the alpha mask vector for the forgotten class and cast to 4D tensor shape
-            final_mask = torch.sigmoid(wf_model.alpha[self.target_class_index]).view(-1, 1, 1, 1)
-            
-            # Apply filter masking permanently back onto the base layer
-            target_conv.weight.copy_(original_weights * final_mask)
-                        
-            # Unfreeze architecture parameters for evaluations downstream
-            for p in model.parameters():
-                p.requires_grad = True
-                        
-            print(f">> Permanently altered {out_channels} convolutional filters in layer4 via WF-Net.")
-        return model