pygip.models.defense.RandomWM

Module Attributes

class pygip.models.defense.RandomWM.RandomWM(dataset, attack_node_fraction=0.2, wm_node=50, pr=0.2, pg=0.2, attack_name=None)

Bases: BaseDefense

A flexible defense implementation using watermarking to protect against model extraction attacks on graph neural networks.

This class combines the functionalities from the original watermark.py: - Generating watermark graphs - Training models on original and watermark graphs - Merging graphs for testing - Evaluating effectiveness against attacks - Dynamic selection of attack methods

_abc_impl = <_abc_data object>

_evaluate_attack_on_watermark(attack_model)

Evaluate how well the attack model performs on the watermark graph.

Parameters:

attack_model (torch.nn.Module) – The model obtained from the attack

Returns:

Attack model’s accuracy on the watermark graph

Return type:

float

_evaluate_watermark(model)

Evaluate watermark detection effectiveness.

Parameters:

model (torch.nn.Module) – The model to evaluate

Returns:

Watermark detection accuracy

Return type:

float

_generate_watermark_graph()

Generate a watermark graph using Erdos-Renyi random graph model.

Returns:

The generated watermark graph

Return type:

dgl.DGLGraph

_get_attack_class(attack_name)

Dynamically import and return the specified attack class.

Parameters:

attack_name (str) – Name of the attack class to import

Returns:

The requested attack class

Return type:

class

_test_on_watermark(model, wm_dataloader)

Test a model’s accuracy on the watermark graph.

Parameters:

• model (torch.nn.Module) – The model to test

• wm_dataloader (DataLoader) – DataLoader for the watermark graph

Returns:

Accuracy on the watermark graph

Return type:

float

_train_defense_model()

Helper function for training a defense model with watermarking.

Returns:

The trained defense model with embedded watermark

Return type:

torch.nn.Module

_train_target_model()

Helper function for training the target model on the original graph.

Returns:

The trained target model

Return type:

torch.nn.Module

defend(attack_name=None)

Main defense workflow: 1. Train a target model on the original graph 2. Attack the target model to establish baseline vulnerability 3. Train a defense model with watermarking 4. Test the defense model against the same attack 5. Print performance metrics

Parameters:

attack_name (str, optional) – Name of the attack class to use, overrides the one set in __init__

Returns:

Dictionary containing performance metrics

Return type:

dict

supported_api_types = {'dgl'}