def main(argv=None): datasets = FLAGS.datasets start=FLAGS.start end=FLAGS.end if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) sample = X_test[start:end] #imsave(FLAGS.sample, deprocess_image_1(sample)) elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, X_test, Y_test = data_cifar10(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_train[0:10000] #imsave(FLAGS.sample, deprocess_image_1(sample)) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[0:100] imsave(FLAGS.sample, deprocess_image_1(sample)) store_path = 'test' suc,total=cw(datasets=datasets, sample=sample, model_name=FLAGS.model, target=FLAGS.target, store_path=store_path, ini_con=0.1,start=start,end=end) print(suc) print(total)
def main(argv=None): datasets = FLAGS.datasets if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) sample = X_test[0:1000] elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[198:199] imsave(FLAGS.sample, deprocess_image_1(sample)) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[198:199] imsave(FLAGS.sample, deprocess_image_1(sample)) store_path = '../datasets/experiment/mnist/fgsm/test' fgsm(datasets=datasets, sample=sample, model_name=FLAGS.model_name, store_path=store_path, step_size=FLAGS.step_size, epoch=FLAGS.epoch)
def main(argv=None): datasets = FLAGS.datasets if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) sample = X_test[0:10] elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, X_test, Y_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[0:2] #imsave('a.png', deprocess_image_3(sample)) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[198:199] imsave(FLAGS.sample, deprocess_image_1(sample)) store_path = 'test0.03' bim(datasets=datasets, sample=sample, model_name=FLAGS.model, store_path=store_path, step_size='0.03')
def main(argv=None): datasets = FLAGS.datasets if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) sample = X_test[0:1] imsave(FLAGS.sample, deprocess_image_1(sample)) elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, X_test, Y_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[198:199] imsave(FLAGS.sample, deprocess_image_1(sample)) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sample = X_test[198:199] imsave(FLAGS.sample, deprocess_image_1(sample)) jsma(datasets=FLAGS.datasets, sample_path=FLAGS.sample, model_name=FLAGS.model, target=FLAGS.target, store_path=FLAGS.store_path)
def get_data(datasets): if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) elif 'cifar10' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, X_test, Y_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) return X_train, Y_train, X_test, Y_test
def mutation_tutorial(datasets, attack, sample_path, store_path, model_path, level=1, test_num=100, mutation_number=1000, mutated=False): if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) sess, preds, x, y, model, feed_dict = model_load(datasets, model_path + datasets) # Generate random matution matrix for mutations store_path = store_path + attack + '/' + datasets + '/' + str(level) if not os.path.exists(store_path): os.makedirs(store_path) result = '' sample_path = sample_path + attack + '/' + datasets [image_list, image_files, real_labels, predicted_labels] = utils.get_data_mutation_test(sample_path) index = np.random.choice(len(image_files), test_num, replace=False) image_list = np.asarray(image_list)[index] image_files = np.asarray(image_files)[index].tolist() predicted_labels = np.asarray(predicted_labels)[index].tolist() seed_number = len(image_list) if datasets == 'mnist': img_rows = 28 img_cols = 28 mutation_test = MutationTest(img_rows, img_cols, seed_number, mutation_number, level) mutation_test.mutation_generate(mutated, store_path, utils.generate_value_1) elif datasets == 'cifar10' or datasets == 'svhn': img_rows = 32 img_cols = 32 mutation_test = MutationTest(img_rows, img_cols, seed_number, mutation_number, level) mutation_test.mutation_generate(mutated, store_path, utils.generate_value_3) store_string, result = mutation_test.mutation_test_adv( preprocess_image_1, result, image_list, predicted_labels, sess, x, preds, image_files, feed_dict) with open(store_path + "/adv_result.csv", "w") as f: f.write(store_string) path = store_path + '/ori_jsma' if not os.path.exists(path): os.makedirs(path) preds_test = np.asarray([]) for i in range(40): preds_test = np.concatenate( (preds_test, model_argmax(sess, x, preds, X_test[i * 250:(i + 1) * 250], feed=feed_dict))) inds_correct = np.asarray(np.where(preds_test == Y_test.argmax(axis=1))[0]) inds_correct = inds_correct[np.random.choice(len(inds_correct), test_num, replace=False)] image_list = X_test[inds_correct] real_labels = Y_test[inds_correct].argmax(axis=1) np.save(path + '/ori_x.npy', np.asarray(image_list)) np.save(path + '/ori_y.npy', np.asarray(real_labels)) image_list = np.load(path + '/ori_x.npy') real_labels = np.load(path + '/ori_y.npy') store_string, result = mutation_test.mutation_test_ori( result, image_list, sess, x, preds, feed_dict) with open(store_path + "/ori_result.csv", "w") as f: f.write(store_string) with open(store_path + "/result.csv", "w") as f: f.write(result) # Close TF session sess.close() print('Finish.')
def detect_adv_samples(datasets, model_path, sample_path, store_path, attack_type): print('Loading the data and model...') # Load the model sess, preds, x, y, model, feed_dict = model_load(datasets, model_path) # # Load the dataset if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) # # Refine the normal, noisy and adversarial sets to only include samples for # # which the original version was correctly classified by the model # preds_test = model_argmax(sess, x, preds, X_test, feed=feed_dict) # inds_correct = np.where(preds_test == Y_test.argmax(axis=1))[0] # X_test = X_test[inds_correct] # X_test = X_test[np.random.choice(len(X_test), 500)]#500 # # # Check attack type, select adversarial and noisy samples accordingly # print('Loading adversarial samples...') # # Load adversarial samplesx # [X_test_adv, adv_image_files, real_labels, predicted_labels] = utils.get_data_mutation_test(sample_path) # X_test_adv = preprocess_image_1(np.asarray(X_test_adv).astype('float32')) # if len(X_test_adv.shape) < 4: # X_test_adv = np.expand_dims(X_test_adv, axis=3) [X_test_adv_train, adv_image_files, real_labels, predicted_labels ] = utils.get_data_mutation_test("../datasets/experiment/" + datasets + "/" + attack_type + "/train") [X_test_adv_test, adv_image_files, real_labels, predicted_labels ] = utils.get_data_mutation_test("../datasets/experiment/" + datasets + "/" + attack_type + "/test") train_num = len(X_test_adv_train) test_num = len(X_test_adv_test) X_test_adv = preprocess_image_1( np.concatenate((np.asarray(X_test_adv_train), np.asarray(X_test_adv_test))).astype('float32')) if len(X_test_adv.shape) < 4: X_test_adv = np.expand_dims(X_test_adv, axis=3) [X_test_train, adv_image_files, real_labels, predicted_labels] = utils.get_data_normal_test("../datasets/experiment/" + datasets + "/normal/train") [X_test_test, adv_image_files, real_labels, predicted_labels] = utils.get_data_normal_test("../datasets/experiment/" + datasets + "/normal/test") X_test_train = np.asarray(X_test_train)[np.random.choice(len(X_test_train), train_num, replace=False)] X_test_test = np.asarray(X_test_test)[np.random.choice(len(X_test_test), test_num, replace=False)] X_test = preprocess_image_1( np.concatenate((np.asarray(X_test_train), np.asarray(X_test_test))).astype('float32')) if len(X_test.shape) < 4: X_test = np.expand_dims(X_test, axis=3) ## Get Bayesian uncertainty scores print('Getting Monte Carlo dropout variance predictions...') uncerts_normal = get_mc_predictions(sess, x, preds, X_test).var(axis=0).mean(axis=1) uncerts_adv = get_mc_predictions(sess, x, preds, X_test_adv).var(axis=0).mean(axis=1) ## Get KDE scores # Get deep feature representations print('Getting deep feature representations...') X_train_features = get_deep_representations(sess, x, X_train, model, feed_dict) X_test_normal_features = get_deep_representations(sess, x, X_test, model, feed_dict) X_test_adv_features = get_deep_representations(sess, x, X_test_adv, model, feed_dict) # Train one KDE per class print('Training KDEs...') class_inds = {} for i in range(Y_train.shape[1]): class_inds[i] = np.where(Y_train.argmax(axis=1) == i)[0] kdes = {} warnings.warn( "Using pre-set kernel bandwidths that were determined " "optimal for the specific CNN models of the paper. If you've " "changed your model, you'll need to re-optimize the " "bandwidth.") for i in range(Y_train.shape[1]): kdes[i] = KernelDensity(kernel='gaussian', bandwidth=BANDWIDTHS[datasets]) \ .fit(X_train_features[class_inds[i]]) # Get model predictions print('Computing model predictions...') preds_test_normal = model_argmax(sess, x, preds, X_test, feed=feed_dict) preds_test_adv = model_argmax(sess, x, preds, X_test_adv, feed=feed_dict) # Get density estimates print('computing densities...') densities_normal = score_samples(kdes, X_test_normal_features, preds_test_normal) densities_adv = score_samples(kdes, X_test_adv_features, preds_test_adv) uncerts_pos = uncerts_adv[:] uncerts_neg = uncerts_normal[:] characteristics, labels = merge_and_generate_labels( uncerts_pos, uncerts_neg) file_name = os.path.join('../detection/bu/', "%s_%s.npy" % (datasets, attack_type)) data = np.concatenate((characteristics, labels), axis=1) np.save(file_name, data) densities_pos = densities_adv[:] densities_neg = densities_normal[:] characteristics, labels = merge_and_generate_labels( densities_pos, densities_neg) file_name = os.path.join( '../detection/de/', "%s_%s_%.4f.npy" % (datasets, attack_type, BANDWIDTHS[datasets])) data = np.concatenate((characteristics, labels), axis=1) np.save(file_name, data) ## Z-score the uncertainty and density values uncerts_normal_z, uncerts_adv_z = normalize(uncerts_normal, uncerts_adv) densities_normal_z, densities_adv_z = normalize(densities_normal, densities_adv) ## Build detector values, labels = features(densities_pos=densities_adv_z, densities_neg=densities_normal_z, uncerts_pos=uncerts_adv_z, uncerts_neg=uncerts_normal_z) X_tr, Y_tr, X_te, Y_te = block_split(values, labels, train_num) lr = train_lr(X_tr, Y_tr) ## Evaluate detector # Compute logistic regression model predictions probs = lr.predict_proba(X_te)[:, 1] preds = lr.predict(X_te) # Compute AUC n_samples = int(len(X_te) / 2) # The first 2/3 of 'probs' is the negative class (normal and noisy samples), # and the last 1/3 is the positive class (adversarial samples). _, _, auc_score = compute_roc(probs_neg=probs[:n_samples], probs_pos=probs[n_samples:]) precision = precision_score(Y_te, preds) recall = recall_score(Y_te, preds) y_label_pred = lr.predict(X_te) acc = accuracy_score(Y_te, y_label_pred) print( 'Detector ROC-AUC score: %0.4f, accuracy: %.4f, precision: %.4f, recall: %.4f' % (auc_score, acc, precision, recall))
def batch_attack(datasets, attack, model_path, store_path, nb_classes): if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) elif 'svhn' == datasets: # choose the method of preprocess image preprocess_image = preprocess_image_1 train_start = 0 train_end = 73257 test_start = 0 test_end = 26032 # Get SVHN test data X_train, Y_train, X_test, Y_test = data_svhn( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) store_path = store_path + attack + '/' + datasets sample_path = '../datasets/integration/batch_attack/' + datasets + '/' sess, preds, x, y, model, feed_dict = model_load(datasets, model_path) if os.listdir(sample_path) == []: for i in range(len(X_test)): sample = X_test[i:i + 1] path = sample_path + str(i) + '.png' imsave(path, deprocess_image_1(sample)) current_img = ndimage.imread(path) img = np.expand_dims( preprocess_image_1(current_img.astype('float64')), 0) p = model_argmax(sess, x, preds, img, feed=feed_dict) if p != Y_test[i].argmax(axis=0): os.remove(path) # for i in range(len(X_test)): # sample = X_test[i:i+1] # if model_argmax(sess, x, preds, sample, feed=feed_dict) == Y_test[i].argmax(axis=0): # path = sample_path + str(i) + '.png' # imsave(path, deprocess_image_1(sample)) sess.close() samples = os.listdir(sample_path) for sample in samples: tf.reset_default_graph() if 'blackbox' == attack: blackbox(datasets=datasets, sample_path=sample_path + sample, model_path=model_path, store_path=store_path, nb_classes=nb_classes) elif 'fgsm' == attack: fgsm(datasets=datasets, sample_path=sample_path + sample, model_path=model_path, store_path=store_path, nb_classes=nb_classes) else: i = int(sample.split('.')[-2]) for j in range(nb_classes): tf.reset_default_graph() if Y_test[i][j] == 0: if 'jsma' == attack: jsma(datasets=datasets, sample_path=sample_path + sample, target=j, model_path=model_path, store_path=store_path, nb_classes=nb_classes) if 'cw' == attack: cw(datasets=datasets, sample_path=sample_path + sample, target=j, model_path=model_path, store_path=store_path, nb_classes=nb_classes)
from nmutant_data.cifar10 import data_cifar10 import tensorflow as tf from nmutant_data.data import get_shape from nmutant_util.utils_imgproc import deprocess_image_1, preprocess_image_1, deprocess_image_1 datasets = 'cifar10' num = 10000 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 preprocess_image = preprocess_image_1 X_train, Y_train, X_test, Y_test = data_cifar10(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) input_shape, nb_classes = get_shape(datasets) sample = X_test[0:num] models3 = ['lenet1', 'lenet4', 'lenet5'] models2 = ['vgg11', 'vgg13', 'vgg16', 'vgg19'] models1 = [ 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'googlenet12', 'googlenet16', 'googlenet22' ] models = [ 'vgg11', 'vgg13', 'vgg16', 'vgg19', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'googlenet12', 'googlenet16', 'googlenet22' ]
def prepare_datasets(datasets, model_path, attack_type, sample_path): print('Loading the data and model...') # Load the model sess, preds, x, y, model, feed_dict = model_load(datasets, model_path) # Load the dataset if 'mnist' == datasets: train_start = 0 train_end = 60000 test_start = 0 test_end = 10000 # Get MNIST test data X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end) elif 'cifar10' == datasets: preprocess_image = preprocess_image_1 train_start = 0 train_end = 50000 test_start = 0 test_end = 10000 # Get CIFAR10 test data X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10( train_start=train_start, train_end=train_end, test_start=test_start, test_end=test_end, preprocess=preprocess_image) if attack_type == "normal": # Refine the normal, noisy and adversarial sets to only include samples for # which the original version was correctly classified by the model preds_test = np.asarray([]) for i in range(40): preds_test = np.concatenate( (preds_test, model_argmax(sess, x, preds, X_test[i * 250:(i + 1) * 250], feed=feed_dict))) inds_correct = np.asarray( np.where(preds_test == Y_test.argmax(axis=1))[0]) inds_correct = inds_correct[np.random.choice(len(inds_correct), 5000, replace=False)] X_test = X_test[inds_correct] for i in range(4000): imsave( "../datasets/experiment/" + datasets + "/normal/train/" + str(inds_correct[i]) + '_' + str(int(preds_test[inds_correct[i]])) + '_' + str(int(preds_test[inds_correct[i]])) + '_.png', deprocess_image_1(X_test[i:i + 1])) for j in range(1000): imsave( "../datasets/experiment/" + datasets + "/normal/test/" + str(inds_correct[4000 + j]) + '_' + str(int(preds_test[inds_correct[4000 + j]])) + '_' + str(int(preds_test[inds_correct[4000 + j]])) + '_.png', deprocess_image_1(X_test[4000 + j:4001 + j])) elif attack_type == "error": preds_test = np.asarray([]) for i in range(40): preds_test = np.concatenate( (preds_test, model_argmax(sess, x, preds, X_test[i * 250:(i + 1) * 250], feed=feed_dict))) inds_correct = np.asarray( np.where(preds_test != Y_test.argmax(axis=1))[0]) X_test = X_test[inds_correct] num = int(len(X_test) * 0.8) for i in range(num): imsave( "../datasets/experiment/" + datasets + "/error/train/" + str(inds_correct[i]) + '_' + str(int(np.argmax(Y_test[inds_correct[i]]))) + '_' + str(int(preds_test[inds_correct[i]])) + '_.png', deprocess_image_1(X_test[i:i + 1])) for j in range(len(X_test) - num): imsave( "../datasets/experiment/" + datasets + "/error/test/" + str(inds_correct[num + j]) + '_' + str(int(np.argmax(Y_test[inds_correct[num + j]]))) + '_' + str(int(preds_test[inds_correct[num + j]])) + '_.png', deprocess_image_1(X_test[num + j:num + 1 + j])) else: # Check attack type, select adversarial and noisy samples accordingly print('Loading adversarial samples...') # Load adversarial samplesx [X_test_adv, adv_image_files, real_labels, predicted_labels ] = utils.get_data_mutation_test(sample_path + attack_type + '/' + datasets) if len(X_test_adv) > 5000: index = np.asarray(range(len(X_test_adv))) index = index[np.random.choice(len(index), 5000, replace=False)] for i in range(4000): imsave( "../datasets/experiment/" + datasets + "/" + attack_type + "/train/" + adv_image_files[index[i]], X_test_adv[index[i]]) for j in range(1000): imsave( "../datasets/experiment/" + datasets + "/" + attack_type + "/test/" + adv_image_files[index[4000 + j]], X_test_adv[index[4000 + j]]) else: index = np.asarray(range(len(X_test_adv))) np.random.shuffle(index) cut = int(len(X_test_adv) * 0.8) for i in range(len(index)): if i < cut: imsave( "../datasets/experiment/" + datasets + "/" + attack_type + "/train/" + adv_image_files[index[i]], X_test_adv[index[i]]) else: imsave( "../datasets/experiment/" + datasets + "/" + attack_type + "/test/" + adv_image_files[index[i]], X_test_adv[index[i]])