OpenAttack

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'''
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This example code shows how to using multiprocessing to accelerate adversarial attacks
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'''
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import OpenAttack
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import datasets
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def dataset_mapping(x):
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    return {
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        "x": x["sentence"],
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        "y": 1 if x["label"] > 0.5 else 0,
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    }
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def main():
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    victim = OpenAttack.loadVictim("BERT.SST")
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    # Victim.BiLSTM.SST is a pytorch model which is trained on Dataset.SST. It uses Glove vectors for word representation.
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    # The load operation returns a PytorchClassifier that can be further used for Attacker and AttackEval.
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    dataset = datasets.load_dataset("sst", split="train[:20]").map(function=dataset_mapping)
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    # Dataset.SST.sample is a list of 1k sentences sampled from test dataset of Dataset.SST.
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    attacker = OpenAttack.attackers.GeneticAttacker()
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    # After this step, we’ve initialized a GeneticAttacker and uses the default configuration during attack process.
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    attack_eval = OpenAttack.AttackEval(attacker, victim)
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    # DefaultAttackEval is the default implementation for AttackEval which supports seven basic metrics.
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    attack_eval.eval(dataset, visualize=True, num_workers=4)
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    # Using multiprocessing by specify num_workers
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if __name__ == "__main__":
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    main()