# Line 7: Return poison data
return D_p, model_t
if __name__ == "__main__":
parser = argparse.ArgumentParser()
# ol: target classifier is from the adapttive attack
# kkt: target is from kkt attack
# real: actual classifier, compare: compare performance
# of kkt attack and adaptive attack using same stop criteria
# Global params
parser.add_argument('--model_arch',
default='lenet',
choices=dnn_utils.get_model_names(),
help='Victim model architecture')
parser.add_argument('--dataset',
default='mnist',
choices=datasets.get_dataset_names(),
help="Which dataset to use?")
parser.add_argument('--batch_size',
default=-1,
type=int,
help='Batch size while training models')
parser.add_argument('--online_alg_criteria',
default='norm',
choices=['max_loss', 'norm'],
help='Stop criteria of online alg: max_loss or norm')
parser.add_argument('--poison_model_path',
type=str,
if args.study_mode:
model, _, _, all_stats = return_data
else:
model, _, _ = return_data
if args.study_mode:
return model, all_stats
return model
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--model_arch',
default='flat',
choices=get_model_names(),
help='Victim model architecture')
parser.add_argument('--dataset',
default='mnist17_first',
choices=datasets.get_dataset_names(),
help="Which dataset to use?")
parser.add_argument('--attacker_goal',
default=0.05,
type=float,
help='desired accuracy on target class')
parser.add_argument('--batch_size',
default=128,
type=int,
help="Batch size while training models"
"Set as -1 to run GD instead of BGD")
parser.add_argument('--poison_class',