pygip.models.attack.DataFreeMEA

Classes

DFEAAttack(dataset, attack_x_ratio, ...[, ...])
DFEATypeI(dataset, attack_x_ratio, ...[, ...])	Type I: Uses victim outputs + gradients for surrogate training.
DFEATypeII(dataset, attack_x_ratio, ...[, ...])	Type II: Uses victim outputs only (hard labels).
DFEATypeIII(dataset, attack_x_ratio, ...[, ...])	Type III: Two surrogates with victim supervision + consistency.
GraphGenerator(node_number, feature_number, ...)

class pygip.models.attack.DataFreeMEA.DFEAAttack(dataset, attack_x_ratio, attack_a_ratio, model_path=None)

Bases: BaseAttack

_abc_impl = <_abc_data object>

_evaluate_on_real_test(surrogate, metric, metric_comp)

Evaluate the surrogate on the real test set and update metrics

_forward(model, graph, features)

_load_model(model_path)

Load a pre-trained model.

_train_target_model()

Train the target model if not provided.

abstract attack()

Execute the attack.

supported_api_types = {'dgl'}

class pygip.models.attack.DataFreeMEA.DFEATypeI(dataset, attack_x_ratio, attack_a_ratio, model_path=None)

Bases: DFEAAttack

Type I: Uses victim outputs + gradients for surrogate training.

_abc_impl = <_abc_data object>

attack()

Execute the attack.

class pygip.models.attack.DataFreeMEA.DFEATypeII(dataset, attack_x_ratio, attack_a_ratio, model_path=None)

Bases: DFEAAttack

Type II: Uses victim outputs only (hard labels).

_abc_impl = <_abc_data object>

attack()

Execute the attack.

class pygip.models.attack.DataFreeMEA.DFEATypeIII(dataset, attack_x_ratio, attack_a_ratio, model_path=None)

Bases: DFEAAttack

Type III: Two surrogates with victim supervision + consistency.

_abc_impl = <_abc_data object>

attack()

Execute the attack.

class pygip.models.attack.DataFreeMEA.GraphGenerator(node_number, feature_number, label_number)

Bases: object

generate()