def main(): with open('data.json') as data_json: data_params = json.load(data_json) parser = argparse.ArgumentParser() parser.add_argument('--data', type=str) parser.add_argument('--data_path', type=str, default='data') parser.add_argument('--output_path', type=str, default='results') parser.add_argument('--pretrained', type=str, required=True) parser.add_argument('--batch_size', type=int, default=128) parser.add_argument('--attack', type=str, required=True, choices=data_params['attacks']) parser.add_argument('--eps', type=float, default=0.3) # NOTE: In CW_L2 attack, eps is the upper bound of c. parser.add_argument('--n_samples', type=int, default=2000) parser.add_argument('--random_state', type=int, default=1234) args = parser.parse_args() print(args) set_seeds(args.random_state) if not os.path.exists(args.output_path): print('Output folder does not exist. Create:', args.output_path) os.mkdir(args.output_path) print('Dataset:', args.data) print('Pretrained model:', args.pretrained) print('Running attack: {}'.format(args.attack)) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Device: {}'.format(device)) # Prepare data transforms = tv.transforms.Compose([tv.transforms.ToTensor()]) if args.data == 'mnist': dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms) elif args.data == 'cifar10': dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms) else: data_path = os.path.join(args.data_path, data_params['data'][args.data]['file_name']) print('Read file:', data_path) X, y = load_csv(data_path) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=data_params['data'][args.data]['n_test'], random_state=args.random_state) scaler = MinMaxScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) dataset_train = TensorDataset(torch.from_numpy(X_train).type(torch.float32), torch.from_numpy(y_train).type(torch.long)) dataset_test = TensorDataset(torch.from_numpy(X_test).type(torch.float32), torch.from_numpy(y_test).type(torch.long)) dataloader_train = DataLoader(dataset_train, 256, shuffle=False) dataloader_test = DataLoader(dataset_test, 256, shuffle=False) shape_train = get_shape(dataloader_train.dataset) shape_test = get_shape(dataloader_test.dataset) print('Train set:', shape_train) print('Test set:', shape_test) # Load model use_prob = args.attack not in ['apgd', 'apgd1', 'apgd2', 'cw2', 'cwinf'] print('Attack:', args.attack) print('Using softmax layer:', use_prob) if args.data == 'mnist': model = BaseModel(use_prob=use_prob).to(device) model_name = 'basic' elif args.data == 'cifar10': model_name = args.pretrained.split('_')[1] if model_name == 'resnet': model = Resnet(use_prob=use_prob).to(device) elif model_name == 'vgg': model = Vgg(use_prob=use_prob).to(device) else: raise ValueError('Unknown model: {}'.format(model_name)) else: n_features = data_params['data'][args.data]['n_features'] n_classes = data_params['data'][args.data]['n_classes'] model = NumericModel( n_features, n_hidden=n_features * 4, n_classes=n_classes, use_prob=use_prob).to(device) model_name = 'basic' + str(n_features * 4) optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4) loss = nn.CrossEntropyLoss() pretrained_path = os.path.join(args.output_path, args.pretrained) model.load_state_dict(torch.load(pretrained_path, map_location=device)) _, acc_train = validate(model, dataloader_train, loss, device) _, acc_test = validate(model, dataloader_test, loss, device) print('Accuracy on train set: {:.4f}%'.format(acc_train * 100)) print('Accuracy on test set: {:.4f}%'.format(acc_test * 100)) # Create a subset which only contains recognisable samples. tensor_test_X, tensor_test_y = get_correct_examples( model, dataset_test, device=device, return_tensor=True) dataset_perfect = TensorDataset(tensor_test_X, tensor_test_y) loader_perfect = DataLoader(dataset_perfect, batch_size=512, shuffle=False) _, acc_perfect = validate(model, loader_perfect, loss, device) print('Accuracy on {} filtered test examples: {:.4f}%'.format( len(dataset_perfect), acc_perfect * 100)) # Generate adversarial examples n_features = data_params['data'][args.data]['n_features'] n_classes = data_params['data'][args.data]['n_classes'] if isinstance(n_features, int): n_features = (n_features,) classifier = PyTorchClassifier( model=model, loss=loss, input_shape=n_features, optimizer=optimizer, nb_classes=n_classes, clip_values=(0.0, 1.0), device_type='gpu') if args.attack == 'apgd': eps_step = args.eps / 10.0 if args.eps <= 0.1 else 0.1 attack = AutoProjectedGradientDescent( estimator=classifier, eps=args.eps, eps_step=eps_step, max_iter=1000, batch_size=args.batch_size, targeted=False) elif args.attack == 'apgd1': attack = AutoProjectedGradientDescent( estimator=classifier, norm=1, eps=args.eps, eps_step=0.1, max_iter=1000, batch_size=args.batch_size, targeted=False) elif args.attack == 'apgd2': attack = AutoProjectedGradientDescent( estimator=classifier, norm=2, eps=args.eps, eps_step=0.1, max_iter=1000, batch_size=args.batch_size, targeted=False) elif args.attack == 'bim': eps_step = args.eps / 10.0 attack = BasicIterativeMethod( estimator=classifier, eps=args.eps, eps_step=eps_step, max_iter=1000, batch_size=args.batch_size, targeted=False) elif args.attack == 'boundary': attack = BoundaryAttack( estimator=classifier, max_iter=1000, sample_size=args.batch_size, targeted=False) elif args.attack == 'cw2': # NOTE: Do NOT increase the batch size! attack = CarliniWagnerAttackL2( model=model, n_classes=n_classes, confidence=args.eps, verbose=True, check_prob=False, batch_size=args.batch_size, targeted=False) elif args.attack == 'cwinf': attack = CarliniLInfMethod( classifier=classifier, confidence=args.eps, max_iter=1000, batch_size=args.batch_size, targeted=False) elif args.attack == 'deepfool': attack = DeepFool( classifier=classifier, epsilon=args.eps, batch_size=args.batch_size) elif args.attack == 'fgsm': attack = FastGradientMethod( estimator=classifier, eps=args.eps, batch_size=args.batch_size) elif args.attack == 'jsma': attack = SaliencyMapMethod( classifier=classifier, gamma=args.eps, batch_size=args.batch_size) elif args.attack == 'line': if args.data == 'mnist': color = args.eps elif args.data == 'cifar10': color = (args.eps, args.eps, args.eps) else: raise NotImplementedError attack = LineAttack(color=color, thickness=1) elif args.attack == 'shadow': attack = ShadowAttack( estimator=classifier, batch_size=args.batch_size, targeted=False, verbose=False) elif args.attack == 'watermark': attack = WaterMarkAttack( eps=args.eps, n_classes=data_params['data'][args.data]['n_classes'], x_min=0.0, x_max=1.0, targeted=False) X_train, y_train = get_correct_examples(model, dataset_train, device=device, return_tensor=True) X_train = X_train.cpu().detach().numpy() y_train = y_train.cpu().detach().numpy() attack.fit(X_train, y_train) else: raise NotImplementedError if len(dataset_perfect) > args.n_samples: n = args.n_samples else: n = len(dataset_perfect) X_benign = tensor_test_X[:n].cpu().detach().numpy() y = tensor_test_y[:n].cpu().detach().numpy() print('Creating {} adversarial examples with eps={} (Not all attacks use eps)'.format(n, args.eps)) time_start = time.time() # Shadow attack only takes single sample! if args.attack == 'shadow': adv = np.zeros_like(X_benign) for i in trange(len(X_benign)): adv[i] = attack.generate(x=np.expand_dims(X_benign[i], axis=0)) elif args.attack == 'watermark': # This is untargeted. adv = attack.generate(X_benign, y) else: adv = attack.generate(x=X_benign) time_elapsed = time.time() - time_start print('Total time spend: {}'.format(str(datetime.timedelta(seconds=time_elapsed)))) pred_benign = np.argmax(classifier.predict(X_benign), axis=1) acc_benign = np.sum(pred_benign == y) / n pred_adv = np.argmax(classifier.predict(adv), axis=1) acc_adv = np.sum(pred_adv == y) / n print("Accuracy on benign samples: {:.4f}%".format(acc_benign * 100)) print("Accuracy on adversarial examples: {:.4f}%".format(acc_adv * 100)) # Save results if args.n_samples < 2000: output_file = '{}_{}_{}_{}_size{}'.format(args.data, model_name, args.attack, str(args.eps), args.n_samples) else: output_file = '{}_{}_{}_{}'.format(args.data, model_name, args.attack, str(args.eps)) path_x = os.path.join(args.output_path, '{}_x.npy'.format(output_file)) path_y = os.path.join(args.output_path, '{}_y.npy'.format(output_file)) path_adv = os.path.join(args.output_path, '{}_adv.npy'.format(output_file)) np.save(path_x, X_benign) np.save(path_y, y) np.save(path_adv, adv) print('Saved to:', '{}_adv.npy'.format(output_file)) print()
def main(): with open('data.json') as data_json: data_params = json.load(data_json) parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, required=True) parser.add_argument('--model', type=str, required=True) parser.add_argument('--pretrained', type=str, required=True) parser.add_argument('--data_path', type=str, default='data') parser.add_argument('--output_path', type=str, default='results') parser.add_argument('--random_state', type=int, default=1234) args = parser.parse_args() print(args) set_seeds(args.random_state) if not os.path.exists(args.output_path): print('Output folder does not exist. Create:', args.output_path) os.mkdir(args.output_path) print('data:', args.data) print('model:', args.model) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Device: {}'.format(device)) # Prepare data transforms = tv.transforms.Compose([tv.transforms.ToTensor()]) if args.data == 'mnist': dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms) elif args.data == 'cifar10': dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms) else: data_path = os.path.join(args.data_path, data_params['data'][args.data]['file_name']) print('Read file:', data_path) X, y = load_csv(data_path) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=data_params['data'][args.data]['n_test'], random_state=args.random_state) scaler = MinMaxScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) dataset_train = TensorDataset( torch.from_numpy(X_train).type(torch.float32), torch.from_numpy(y_train).type(torch.long)) dataset_test = TensorDataset( torch.from_numpy(X_test).type(torch.float32), torch.from_numpy(y_test).type(torch.long)) loader_train = DataLoader(dataset_train, batch_size=512, shuffle=False) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) shape_train = get_shape(loader_train.dataset) shape_test = get_shape(loader_test.dataset) print('Train set:', shape_train) print('Test set:', shape_test) use_prob = True print('Using softmax layer:', use_prob) # Load model if args.data == 'mnist': model = BaseModel(use_prob=use_prob).to(device) model_name = 'basic' elif args.data == 'cifar10': model_name = args.pretrained.split('_')[1] if model_name == 'resnet': model = Resnet(use_prob=use_prob).to(device) elif model_name == 'vgg': model = Vgg(use_prob=use_prob).to(device) else: raise NotImplementedError else: n_features = data_params['data'][args.data]['n_features'] n_classes = data_params['data'][args.data]['n_classes'] model = NumericModel(n_features, n_hidden=n_features * 4, n_classes=n_classes, use_prob=use_prob).to(device) model_name = 'basic' + str(n_features * 4) loss = nn.CrossEntropyLoss() pretrained_path = os.path.join(args.output_path, args.pretrained) model.load_state_dict(torch.load(pretrained_path, map_location=device)) _, acc_train = validate(model, loader_train, loss, device) _, acc_test = validate(model, loader_test, loss, device) print('Accuracy on train set: {:.4f}%'.format(acc_train * 100)) print('Accuracy on test set: {:.4f}%'.format(acc_test * 100)) # Create a subset which only contains recognisable samples. # The original train and test sets are no longer needed. tensor_train_X, tensor_train_y = get_correct_examples(model, dataset_train, device=device, return_tensor=True) dataset_train = TensorDataset(tensor_train_X, tensor_train_y) loader_train = DataLoader(dataset_train, batch_size=512, shuffle=False) _, acc_perfect = validate(model, loader_train, loss, device) print('Accuracy on {} filtered train set: {:.4f}%'.format( len(dataset_train), acc_perfect * 100)) tensor_test_X, tensor_test_y = get_correct_examples(model, dataset_test, device=device, return_tensor=True) dataset_test = TensorDataset(tensor_test_X, tensor_test_y) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) _, acc_perfect = validate(model, loader_test, loss, device) print('Accuracy on {} filtered test set: {:.4f}%'.format( len(dataset_test), acc_perfect * 100)) X_train = tensor_train_X.cpu().detach().numpy() y_train = tensor_train_y.cpu().detach().numpy() X_baard = baard_preprocess(args.data, tensor_train_X).cpu().detach().numpy() obj = {'X_train': X_baard, 'y_train': y_train} path_ouput = os.path.join( args.output_path, '{}_{}_baard_train.pt'.format(args.data, args.model, args.model)) torch.save(obj, path_ouput) print('Save to:', path_ouput) print()
def main(): with open('data.json') as data_json: data_params = json.load(data_json) parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, required=True) parser.add_argument('--data_path', type=str, default='data') parser.add_argument('--output_path', type=str, default='results') parser.add_argument('--pretrained', type=str, required=True) parser.add_argument('--adv', type=str, required=True, help="Example: 'mnist_basic_apgd_0.3'") parser.add_argument('--random_state', type=int, default=1234) args = parser.parse_args() print(args) set_seeds(args.random_state) print('Dataset:', args.data) print('Pretrained model:', args.pretrained) print('Pretrained samples:', args.adv + '_adv.npy') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Device: {}'.format(device)) # Prepare data transforms = tv.transforms.Compose([tv.transforms.ToTensor()]) if args.data == 'mnist': dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms) elif args.data == 'cifar10': dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms) else: data_path = os.path.join(args.data_path, data_params['data'][args.data]['file_name']) print('Read file:', data_path) X, y = load_csv(data_path) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=data_params['data'][args.data]['n_test'], random_state=args.random_state) scaler = MinMaxScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) dataset_train = TensorDataset( torch.from_numpy(X_train).type(torch.float32), torch.from_numpy(y_train).type(torch.long)) dataset_test = TensorDataset( torch.from_numpy(X_test).type(torch.float32), torch.from_numpy(y_test).type(torch.long)) # Note: Train set alway shuffle! loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) shape_train = get_shape(loader_train.dataset) shape_test = get_shape(loader_test.dataset) print('Train set:', shape_train) print('Test set:', shape_test) use_prob = True print('Using softmax layer:', use_prob) n_classes = data_params['data'][args.data]['n_classes'] # Load model if args.data == 'mnist': model = BaseModel(use_prob=use_prob).to(device) model_name = 'basic' elif args.data == 'cifar10': model_name = args.pretrained.split('_')[1] if model_name == 'resnet': model = Resnet(use_prob=use_prob).to(device) elif model_name == 'vgg': model = Vgg(use_prob=use_prob).to(device) else: raise ValueError('Unknown model: {}'.format(model_name)) else: n_features = data_params['data'][args.data]['n_features'] model = NumericModel(n_features, n_hidden=n_features * 4, n_classes=n_classes, use_prob=use_prob).to(device) model_name = 'basic' + str(n_features * 4) loss = nn.CrossEntropyLoss() pretrained_path = os.path.join(args.output_path, args.pretrained) model.load_state_dict(torch.load(pretrained_path)) _, acc_train = validate(model, loader_train, loss, device) _, acc_test = validate(model, loader_test, loss, device) print('Accuracy on train set: {:.4f}%'.format(acc_train * 100)) print('Accuracy on test set: {:.4f}%'.format(acc_test * 100)) # Create a subset which only contains recognisable samples. # The original train and test sets are no longer needed. tensor_train_X, tensor_train_y = get_correct_examples(model, dataset_train, device=device, return_tensor=True) dataset_train = TensorDataset(tensor_train_X, tensor_train_y) loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True) _, acc_perfect = validate(model, loader_train, loss, device) print('Accuracy on {} filtered train set: {:.4f}%'.format( len(dataset_train), acc_perfect * 100)) tensor_test_X, tensor_test_y = get_correct_examples(model, dataset_test, device=device, return_tensor=True) dataset_test = TensorDataset(tensor_test_X, tensor_test_y) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) _, acc_perfect = validate(model, loader_test, loss, device) print('Accuracy on {} filtered test set: {:.4f}%'.format( len(dataset_test), acc_perfect * 100)) # Load pre-trained adversarial examples path_benign = os.path.join(args.output_path, args.adv + '_x.npy') path_adv = os.path.join(args.output_path, args.adv + '_adv.npy') path_y = os.path.join(args.output_path, args.adv + '_y.npy') X_benign = np.load(path_benign) adv = np.load(path_adv) y_true = np.load(path_y) dataset = TensorDataset(torch.from_numpy(X_benign), torch.from_numpy(y_true)) loader = DataLoader(dataset, batch_size=512, shuffle=False) _, acc = validate(model, loader, loss, device) print('Accuracy on {} benign samples: {:.4f}%'.format( len(dataset), acc * 100)) dataset = TensorDataset(torch.from_numpy(adv), torch.from_numpy(y_true)) loader = DataLoader(dataset, batch_size=512, shuffle=False) _, acc = validate(model, loader, loss, device) print('Accuracy on {} adversarial examples: {:.4f}%'.format( len(dataset), acc * 100)) # Do NOT shuffle the indices, so different defences can use the same test set. dataset = TensorDataset(torch.from_numpy(adv)) loader = DataLoader(dataset, batch_size=512, shuffle=False) pred_adv = predict(model, loader, device).cpu().detach().numpy() # Find the thresholds using the 2nd half n = len(X_benign) // 2 # Merge benign samples and adversarial examples into one set. # This labels indicate a sample is an adversarial example or not. X_val, labels_val = merge_and_generate_labels(adv[n:], X_benign[n:], flatten=False) # The predictions for benign samples are exactly same as the true labels. pred_val = np.concatenate((pred_adv[n:], y_true[n:])) X_train = tensor_train_X.cpu().detach().numpy() y_train = tensor_train_y.cpu().detach().numpy() # Train defence time_start = time.time() detector = RegionBasedClassifier(model=model, r=0.2, sample_size=1000, n_classes=n_classes, x_min=0.0, x_max=1.0, batch_size=512, r0=0.0, step_size=0.02, stop_value=0.4, device=device) r_best = detector.search_thresholds(X_val, pred_val, labels_val, verbose=0) time_elapsed = time.time() - time_start print('Total training time:', str(datetime.timedelta(seconds=time_elapsed))) param = { "r": r_best, "sample_size": 1000, "batch_size": 512, "r0": 0, "step_size": 0.02, "stop_value": 0.40 } path_json = os.path.join( 'params', 'rc_param_{}_{}.json'.format(args.data, args.model)) with open(path_json, 'w') as f: json.dump(param, f) print('Save to:', path_json) print()
def main(): with open('data.json') as data_json: data_params = json.load(data_json) parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, required=True) parser.add_argument('--data_path', type=str, default='data') parser.add_argument('--output_path', type=str, default='results') parser.add_argument('--pretrained', type=str, required=True) parser.add_argument('--adv', type=str, required=True, help="Example: 'mnist_basic_apgd_0.3'") parser.add_argument('--defence', type=str, required=True, choices=data_params['defences']) parser.add_argument('--param', type=str, required=True) parser.add_argument('--suffix', type=str) parser.add_argument('--random_state', type=int, default=1234) parser.add_argument('--save', type=int, default=1, choices=[0, 1]) args = parser.parse_args() print(args) set_seeds(args.random_state) if not os.path.exists(args.output_path): print('Output folder does not exist. Create:', args.output_path) os.mkdir(args.output_path) print('Dataset:', args.data) print('Pretrained model:', args.pretrained) print('Pretrained samples:', args.adv + '_adv.npy') print('Defence:', args.defence) with open(args.param) as param_json: param = json.load(param_json) param['n_classes'] = data_params['data'][args.data]['n_classes'] print('Param:', param) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Device: {}'.format(device)) # Prepare data transforms = tv.transforms.Compose([tv.transforms.ToTensor()]) if args.data == 'mnist': dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms) elif args.data == 'cifar10': dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms) else: data_path = os.path.join(args.data_path, data_params['data'][args.data]['file_name']) print('Read file:', data_path) X, y = load_csv(data_path) X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=data_params['data'][args.data]['n_test'], random_state=args.random_state) scaler = MinMaxScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) dataset_train = TensorDataset(torch.from_numpy(X_train).type(torch.float32), torch.from_numpy(y_train).type(torch.long)) dataset_test = TensorDataset(torch.from_numpy(X_test).type(torch.float32), torch.from_numpy(y_test).type(torch.long)) loader_train = DataLoader(dataset_train, batch_size=512, shuffle=False) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) shape_train = get_shape(loader_train.dataset) shape_test = get_shape(loader_test.dataset) print('Train set:', shape_train) print('Test set:', shape_test) use_prob = True print('Using softmax layer:', use_prob) # Load model if args.data == 'mnist': model = BaseModel(use_prob=use_prob).to(device) model_name = 'basic' elif args.data == 'cifar10': model_name = args.pretrained.split('_')[1] if model_name == 'resnet': model = Resnet(use_prob=use_prob).to(device) elif model_name == 'vgg': model = Vgg(use_prob=use_prob).to(device) else: raise ValueError('Unknown model: {}'.format(model_name)) else: n_features = data_params['data'][args.data]['n_features'] n_classes = data_params['data'][args.data]['n_classes'] model = NumericModel(n_features, n_hidden=n_features * 4, n_classes=n_classes, use_prob=use_prob).to(device) model_name = 'basic' + str(n_features * 4) loss = nn.CrossEntropyLoss() pretrained_path = os.path.join(args.output_path, args.pretrained) model.load_state_dict(torch.load(pretrained_path, map_location=device)) _, acc_train = validate(model, loader_train, loss, device) _, acc_test = validate(model, loader_test, loss, device) print('Accuracy on train set: {:.4f}%'.format(acc_train * 100)) print('Accuracy on test set: {:.4f}%'.format(acc_test * 100)) # Create a subset which only contains recognisable samples. # The original train and test sets are no longer needed. tensor_train_X, tensor_train_y = get_correct_examples(model, dataset_train, device=device, return_tensor=True) dataset_train = TensorDataset(tensor_train_X, tensor_train_y) loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True) _, acc_perfect = validate(model, loader_train, loss, device) print('Accuracy on {} filtered train set: {:.4f}%'.format(len(dataset_train), acc_perfect * 100)) tensor_test_X, tensor_test_y = get_correct_examples(model, dataset_test, device=device, return_tensor=True) dataset_test = TensorDataset(tensor_test_X, tensor_test_y) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) _, acc_perfect = validate(model, loader_test, loss, device) print('Accuracy on {} filtered test set: {:.4f}%'.format(len(dataset_test), acc_perfect * 100)) # Load pre-trained adversarial examples path_benign = os.path.join(args.output_path, args.adv + '_x.npy') path_adv = os.path.join(args.output_path, args.adv + '_adv.npy') path_y = os.path.join(args.output_path, args.adv + '_y.npy') X_benign = np.load(path_benign) adv = np.load(path_adv) y_true = np.load(path_y) dataset = TensorDataset(torch.from_numpy(X_benign), torch.from_numpy(y_true)) loader = DataLoader(dataset, batch_size=512, shuffle=False) _, acc = validate(model, loader, loss, device) print('Accuracy on {} benign samples: {:.4f}%'.format(len(dataset), acc * 100)) dataset = TensorDataset(torch.from_numpy(adv), torch.from_numpy(y_true)) loader = DataLoader(dataset, batch_size=512, shuffle=False) _, acc = validate(model, loader, loss, device) print('Accuracy on {} adversarial examples: {:.4f}%'.format(len(dataset), acc * 100)) # Do NOT shuffle the indices, so different defences can use the same test set. dataset = TensorDataset(torch.from_numpy(adv)) loader = DataLoader(dataset, batch_size=512, shuffle=False) pred_adv = predict(model, loader, device).cpu().detach().numpy() # Find the thresholds using the 2nd half n = len(X_benign) // 2 # Merge benign samples and adversarial examples into one set. # This labels indicate a sample is an adversarial example or not. X_val, labels_val = merge_and_generate_labels(adv[n:], X_benign[n:], flatten=False) # The predictions for benign samples are exactly same as the true labels. pred_val = np.concatenate((pred_adv[n:], y_true[n:])) X_train = tensor_train_X.cpu().detach().numpy() y_train = tensor_train_y.cpu().detach().numpy() # Train defence time_start = time.time() if args.defence == 'baard': sequence = param['sequence'] stages = [] if sequence[0]: stages.append(ApplicabilityStage(n_classes=param['n_classes'], quantile=param['q1'])) if sequence[1]: stages.append(ReliabilityStage(n_classes=param['n_classes'], k=param['k_re'], quantile=param['q2'])) if sequence[2]: stages.append(DecidabilityStage(n_classes=param['n_classes'], k=param['k_de'], quantile=param['q3'])) print('BAARD: # of stages:', len(stages)) detector = BAARDOperator(stages=stages) # Run preprocessing baard_train_path = os.path.join(args.output_path, '{}_{}_baard_train.pt'.format(args.data, model_name)) obj = torch.load(baard_train_path) X_baard = obj['X_train'] y_train = obj['y_train'] # Fit the model with the filtered the train set. detector.stages[0].fit(X_baard, y_train) detector.stages[1].fit(X_train, y_train) if len(detector.stages) == 3: detector.stages[2].fit(X_train, y_train) detector.search_thresholds(X_val, pred_val, labels_val) path_baard = os.path.join(args.output_path, 'baard_{}_{}_param.pt'.format(args.data, model_name)) detector.save(path_baard) elif args.defence == 'fs': squeezers = [] if args.data == 'mnist': squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=1)) squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2)) elif args.data == 'cifar10': squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=4)) squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2)) squeezers.append(NLMeansColourSqueezer(x_min=0.0, x_max=1.0, h=2, templateWindowsSize=3, searchWindowSize=13)) else: raise NotImplementedError print('FS: # of squeezers:', len(squeezers)) detector = FeatureSqueezingTorch( classifier=model, lr=0.001, momentum=0.9, weight_decay=5e-4, loss=loss, batch_size=128, x_min=0.0, x_max=1.0, squeezers=squeezers, n_classes=param['n_classes'], device=device) path_fs = os.path.join(args.output_path, '{}_fs.pt'.format(args.pretrained.split('.')[0])) detector.load(path_fs) detector.search_thresholds(X_val, pred_val, labels_val) elif args.defence == 'lid': # This batch_size is not same as the mini batch size for the neural network. before_softmax = args.data == 'cifar10' detector = LidDetector( model, k=param['k'], batch_size=param['batch_size'], x_min=0.0, x_max=1.0, device=device, before_softmax=before_softmax) # LID uses different training set X_train, y_train = detector.get_train_set(X_benign[n:], adv[n:], std_dominator=param['std_dominator']) detector.fit(X_train, y_train, verbose=1) elif args.defence == 'magnet': magnet_detectors = [] # Different datasets require different autoencoders. if args.data == 'mnist': # autoencoder1 and autoencoder2 magnet_detectors.append(MagNetDetector( encoder=Autoencoder1(n_channel=1), classifier=model, lr=param['lr'], batch_size=param['batch_size'], weight_decay=param['weight_decay'], x_min=0.0, x_max=1.0, noise_strength=param['noise_strength'], algorithm='error', p=1, device=device)) magnet_detectors.append(MagNetDetector( encoder=Autoencoder2(n_channel=1), classifier=model, lr=param['lr'], batch_size=param['batch_size'], weight_decay=param['weight_decay'], x_min=0.0, x_max=1.0, noise_strength=param['noise_strength'], algorithm='error', p=2, device=device)) elif args.data == 'cifar10': autoencoder = Autoencoder2( n_channel=data_params['data'][args.data]['n_features'][0]) # There are 3 autoencoder based detectors, but they use the same architecture. magnet_detectors.append(MagNetDetector( encoder=autoencoder, classifier=model, lr=param['lr'], batch_size=param['batch_size'], weight_decay=param['weight_decay'], x_min=0.0, x_max=1.0, noise_strength=param['noise_strength'], algorithm='error', p=2, device=device)) magnet_detectors.append(MagNetDetector( encoder=autoencoder, classifier=model, lr=param['lr'], batch_size=param['batch_size'], weight_decay=param['weight_decay'], x_min=0.0, x_max=1.0, noise_strength=param['noise_strength'], algorithm='prob', temperature=10, device=device)) magnet_detectors.append(MagNetDetector( encoder=autoencoder, classifier=model, lr=param['lr'], batch_size=param['batch_size'], weight_decay=param['weight_decay'], x_min=0.0, x_max=1.0, noise_strength=param['noise_strength'], algorithm='prob', temperature=40, device=device)) else: raise ValueError('Magnet requires autoencoder.') for i, ae in enumerate(magnet_detectors, start=1): ae_path = os.path.join(args.output_path, 'autoencoder_{}_{}_{}.pt'.format(args.data, model_name, i)) ae.load(ae_path) tensor_X_test, _ = dataset2tensor(dataset_test) X_test = tensor_X_test.cpu().detach().numpy() print('Autoencoder {} MSE training set: {:.6f}, test set: {:.6f}'.format(i, ae.score(X_train), ae.score(X_test))) print('Autoencoder {} threshold: {}'.format(i, ae.threshold)) reformer = MagNetAutoencoderReformer( encoder=magnet_detectors[0].encoder, batch_size=param['batch_size'], device=device) detector = MagNetOperator( classifier=model, detectors=magnet_detectors, reformer=reformer, batch_size=param['batch_size'], device=device) elif args.defence == 'rc': detector = RegionBasedClassifier( model=model, r=param['r'], sample_size=param['sample_size'], n_classes=param['n_classes'], x_min=0.0, x_max=1.0, batch_size=param['batch_size'], r0=param['r0'], step_size=param['step_size'], stop_value=param['stop_value'], device=device) # Region-based classifier only uses benign samples to search threshold. # The r value is already set to the optimal. We don't need to search it. # detector.search_thresholds(X_val, pred_val, labels_val, verbose=0) else: raise ValueError('{} is not supported!'.format(args.defence)) time_elapsed = time.time() - time_start print('Total training time:', str(datetime.timedelta(seconds=time_elapsed))) # Test defence time_start = time.time() X_test, labels_test = merge_and_generate_labels(adv[:n], X_benign[:n], flatten=False) pred_test = np.concatenate((pred_adv[:n], y_true[:n])) y_test = np.concatenate((y_true[:n], y_true[:n])) # Only MegNet uses reformer. X_reformed = None if args.defence == 'magnet': X_reformed, res_test = detector.detect(X_test, pred_test) y_pred = predict_numpy(model, X_reformed, device) elif args.defence == 'rc': y_pred = detector.detect(X_test, pred_test) res_test = np.zeros_like(y_pred) else: res_test = detector.detect(X_test, pred_test) y_pred = pred_test acc = acc_on_adv(y_pred[:n], y_test[:n], res_test[:n]) if args.defence == 'rc': fpr = np.mean(y_pred[n:] != y_test[n:]) else: fpr = np.mean(res_test[n:]) print('Acc_on_adv:', acc) print('FPR:', fpr) time_elapsed = time.time() - time_start print('Total test time:', str(datetime.timedelta(seconds=time_elapsed))) # Save results suffix = '_' + args.suffix if args.suffix is not None else '' if args.save: path_result = os.path.join(args.output_path, '{}_{}{}.pt'.format(args.adv, args.defence, suffix)) torch.save({ 'X_val': X_val, 'y_val': np.concatenate((y_true[n:], y_true[n:])), 'labels_val': labels_val, 'X_test': X_test, 'y_test': y_test, 'labels_test': labels_test, 'res_test': y_pred if args.defence == 'rc' else res_test, 'X_reformed': X_reformed, 'param': param}, path_result) print('Saved to:', path_result) else: print('No file is save!') print()
def main(): parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, required=True, choices=['mnist', 'cifar10']) parser.add_argument('--data_path', type=str, default='data') parser.add_argument('--output_path', type=str, default='results') parser.add_argument('--pretrained', type=str, required=True) parser.add_argument('--param', type=str, required=True) parser.add_argument('--random_state', type=int, default=1234) args = parser.parse_args() print(args) set_seeds(args.random_state) if not os.path.exists(args.output_path): print('Output folder does not exist. Create:', args.output_path) os.mkdir(args.output_path) print('Dataset:', args.data) print('Pretrained model:', args.pretrained) with open(args.param) as param_json: param = json.load(param_json) param['n_classes'] = 10 print('Param:', param) device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Device: {}'.format(device)) # Prepare data transforms = tv.transforms.Compose([tv.transforms.ToTensor()]) if args.data == 'mnist': dataset_train = datasets.MNIST(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.MNIST(args.data_path, train=False, download=True, transform=transforms) elif args.data == 'cifar10': dataset_train = datasets.CIFAR10(args.data_path, train=True, download=True, transform=transforms) dataset_test = datasets.CIFAR10(args.data_path, train=False, download=True, transform=transforms) else: raise ValueError('{} is not supported.'.format(args.data)) # Note: Train set alway shuffle! loader_train = DataLoader(dataset_train, batch_size=512, shuffle=True) loader_test = DataLoader(dataset_test, batch_size=512, shuffle=False) shape_train = get_shape(loader_train.dataset) shape_test = get_shape(loader_test.dataset) print('Train set:', shape_train) print('Test set:', shape_test) use_prob = True print('Using softmax layer:', use_prob) # Load model if args.data == 'mnist': model = BaseModel(use_prob=use_prob).to(device) model_name = 'basic' else: # args.data == 'cifar10': model_name = args.pretrained.split('_')[1] if model_name == 'resnet': model = Resnet(use_prob=use_prob).to(device) elif model_name == 'vgg': model = Vgg(use_prob=use_prob).to(device) else: raise ValueError('Unknown model: {}'.format(model_name)) loss = nn.CrossEntropyLoss() pretrained_path = os.path.join(args.output_path, args.pretrained) model.load_state_dict(torch.load(pretrained_path)) _, acc_train = validate(model, loader_train, loss, device) _, acc_test = validate(model, loader_test, loss, device) print('Accuracy on train set: {:.4f}%'.format(acc_train * 100)) print('Accuracy on test set: {:.4f}%'.format(acc_test * 100)) tensor_train_X, tensor_train_y = dataset2tensor(dataset_train) X_train = tensor_train_X.cpu().detach().numpy() y_train = tensor_train_y.cpu().detach().numpy() # Train defence squeezers = [] if args.data == 'mnist': squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=1)) squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2)) else: # CIFAR10 squeezers.append(DepthSqueezer(x_min=0.0, x_max=1.0, bit_depth=4)) squeezers.append(MedianSqueezer(x_min=0.0, x_max=1.0, kernel_size=2)) squeezers.append(NLMeansColourSqueezer(x_min=0.0, x_max=1.0, h=2, templateWindowsSize=3, searchWindowSize=13)) print('FS: # of squeezers:', len(squeezers)) detector = FeatureSqueezingTorch( classifier=model, lr=0.001, momentum=0.9, weight_decay=5e-4, loss=loss, batch_size=128, x_min=0.0, x_max=1.0, squeezers=squeezers, n_classes=param['n_classes'], device=device) detector.fit(X_train, y_train, epochs=param['epochs'], verbose=1) path_fs = os.path.join(args.output_path, '{}_fs.pt'.format(args.pretrained.split('.')[0])) detector.save(path_fs) print('Saved fs to:', path_fs) print()