| import numpy as np | |
| # ---------------------attack config------------------------# | |
| attack_params = { | |
| "FGSM_MNIST": { | |
| 'epsilon': 0.2, | |
| 'order': np.inf, | |
| 'clip_max': None, | |
| 'clip_min': None | |
| }, | |
| "PGD_CIFAR10": { | |
| 'epsilon': 0.1, | |
| 'clip_max': 1.0, | |
| 'clip_min': 0.0, | |
| 'print_process': True | |
| }, | |
| "LBFGS_MNIST": { | |
| 'epsilon': 1e-4, | |
| 'maxiter': 20, | |
| 'clip_max': 1, | |
| 'clip_min': 0, | |
| 'class_num': 10 | |
| }, | |
| "CW_MNIST": { | |
| 'confidence': 1e-4, | |
| 'clip_max': 1, | |
| 'clip_min': 0, | |
| 'max_iterations': 1000, | |
| 'initial_const': 1e-2, | |
| 'binary_search_steps': 5, | |
| 'learning_rate': 5e-3, | |
| 'abort_early': True, | |
| } | |
| } | |
| #-----------defense(Adversarial training) config------------# | |
| defense_params = { | |
| "PGDtraining_MNIST":{ | |
| 'save_dir': "./defense_model", | |
| 'save_model': True, | |
| 'save_name' : "mnist_pgdtraining_0.3.pt", | |
| 'epsilon' : 0.3, | |
| 'epoch_num' : 80, | |
| 'lr' : 0.01 | |
| }, | |
| "FGSMtraining_MNIST":{ | |
| 'save_dir': "./defense_model", | |
| 'save_model': True, | |
| 'save_name' : "mnist_fgsmtraining_0.2.pt", | |
| 'epsilon' : 0.2, | |
| 'epoch_num' : 50, | |
| 'lr_train' : 0.001 | |
| }, | |
| "FAST_MNIST":{ | |
| 'save_dir': "./defense_model", | |
| 'save_model': True, | |
| 'save_name' : "fast_mnist_0.3.pt", | |
| 'epsilon' : 0.3, | |
| 'epoch_num' : 50, | |
| 'lr_train' : 0.001 | |
| } | |
| } | |