Black box

Tune_new.py

@@ -165,65 +165,6 @@ def log_metrics(evaluation_domains, reloaded, strategy_in_use):
             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, suite_runner=None):
     """
@@ -393,8 +334,8 @@ if __name__ == "__main__":
                 #dist_attacker.run_adversarial_evaluation()
                 #dist_attacker.run_incremental_evaluation(current_class_step=i)
 
-            if suite_runner is not None:
-                suite_runner.shutdown_hook()
+            #if suite_runner is not None:
+                #suite_runner.shutdown_hook()
 
         except KeyboardInterrupt:
             print("\nprogram interrupted. Exit!")