diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..c6b359f --- /dev/null +++ b/.gitignore @@ -0,0 +1,2 @@ +sauvegarde/ +save/ \ No newline at end of file diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..56f233d --- /dev/null +++ b/Dockerfile @@ -0,0 +1,19 @@ +# Utiliser une image avec Python 3.6 préinstallé +FROM python:3.6-slim + +# Mettre à jour le système et installer les outils nécessaires +RUN apt-get update && apt-get install -y \ + build-essential \ + && apt-get clean + +# Installer TensorFlow et Keras (versions spécifiques) +RUN pip install tensorflow==1.10.1 keras==2.2.2 + +# Créer un dossier pour la sauvegarde des résultats +RUN mkdir -p /app/save + +# Définir le répertoire de travail +WORKDIR /app + +# Commande par défaut : ouvrir un terminal interactif +CMD ["bash"] diff --git a/alg/__pycache__/lowerbound_functions.cpython-36.pyc b/alg/__pycache__/lowerbound_functions.cpython-36.pyc new file mode 100644 index 0000000..d0afdb0 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a/cleverhans/cleverhans.egg-info/SOURCES.txt b/cleverhans/cleverhans.egg-info/SOURCES.txt new file mode 100644 index 0000000..5985150 --- /dev/null +++ b/cleverhans/cleverhans.egg-info/SOURCES.txt @@ -0,0 +1,15 @@ +README.md +setup.py +cleverhans/__init__.py +cleverhans/attacks.py +cleverhans/attacks_tf.py +cleverhans/model.py +cleverhans/utils.py +cleverhans/utils_keras.py +cleverhans/utils_mnist.py +cleverhans/utils_tf.py +cleverhans.egg-info/PKG-INFO +cleverhans.egg-info/SOURCES.txt +cleverhans.egg-info/dependency_links.txt +cleverhans.egg-info/requires.txt +cleverhans.egg-info/top_level.txt \ No newline at end of file diff --git a/cleverhans/cleverhans.egg-info/dependency_links.txt b/cleverhans/cleverhans.egg-info/dependency_links.txt new file mode 100644 index 0000000..8b13789 --- /dev/null +++ b/cleverhans/cleverhans.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/cleverhans/cleverhans.egg-info/requires.txt b/cleverhans/cleverhans.egg-info/requires.txt new file mode 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b/cleverhans/cleverhans/utils_mnist.py index 24f9b3d..c36c291 100644 --- a/cleverhans/cleverhans/utils_mnist.py +++ b/cleverhans/cleverhans/utils_mnist.py @@ -27,6 +27,7 @@ def data_mnist(datadir='/tmp/', train_start=0, train_end=60000, test_start=0, assert isinstance(test_start, int) assert isinstance(test_end, int) + '''old from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(datadir, one_hot=True, reshape=False) X_train = np.vstack((mnist.train.images, mnist.validation.images)) @@ -34,6 +35,30 @@ def data_mnist(datadir='/tmp/', train_start=0, train_end=60000, test_start=0, X_test = mnist.test.images Y_test = mnist.test.labels + X_train = X_train[train_start:train_end] + Y_train = Y_train[train_start:train_end] + X_test = X_test[test_start:test_end] + Y_test = Y_test[test_start:test_end]''' + + from keras.datasets import fashion_mnist + + # Load data from keras + (X_train, Y_train), (X_test, Y_test) = fashion_mnist.load_data() + + # Normalize data to [0, 1] range + X_train = X_train.astype('float32') / 255.0 + X_test = X_test.astype('float32') / 255.0 + + # Add channel dimension (28x28 -> 28x28x1) + X_train = np.expand_dims(X_train, axis=-1) + X_test = np.expand_dims(X_test, axis=-1) + + # One-hot encode labels + from keras.utils import to_categorical + Y_train = to_categorical(Y_train, 10) + Y_test = to_categorical(Y_test, 10) + + # Apply slicing X_train = X_train[train_start:train_end] Y_train = Y_train[train_start:train_end] X_test = X_test[test_start:test_end] diff --git a/models/__pycache__/conv_encoder_mnist.cpython-312.pyc b/models/__pycache__/conv_encoder_mnist.cpython-312.pyc new file mode 100644 index 0000000..c74cf97 Binary files /dev/null and b/models/__pycache__/conv_encoder_mnist.cpython-312.pyc differ diff --git a/models/__pycache__/conv_encoder_mnist.cpython-36.pyc b/models/__pycache__/conv_encoder_mnist.cpython-36.pyc new file mode 100644 index 0000000..92ad8c8 Binary files /dev/null and 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a/test_attacks/adversarial_images/mosaic_results_with_labels.png b/test_attacks/adversarial_images/mosaic_results_with_labels.png new file mode 100644 index 0000000..2c01e77 Binary files /dev/null and b/test_attacks/adversarial_images/mosaic_results_with_labels.png differ diff --git a/test_attacks/adversarial_images/mosaic_results_with_originals.png b/test_attacks/adversarial_images/mosaic_results_with_originals.png new file mode 100644 index 0000000..9e5bfbf Binary files /dev/null and b/test_attacks/adversarial_images/mosaic_results_with_originals.png differ diff --git a/test_attacks/correct_indexes.py b/test_attacks/correct_indexes.py new file mode 100644 index 0000000..064c5d6 --- /dev/null +++ b/test_attacks/correct_indexes.py @@ -0,0 +1,107 @@ +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +from __future__ import unicode_literals + +import numpy as np +import tensorflow as tf +import os +import pickle +from tensorflow.python.platform import flags +import sys + +sys.path.append('../utils/') +sys.path.append('../cleverhans/') +from cleverhans.utils import set_log_level +from model_eval import model_eval + +import keras.backend +sys.path.append('load/') +from load_classifier import load_classifier + +FLAGS = flags.FLAGS + +def extract_correct_indices(data_name, model_name, batch_size=128): + """ + Extract indices of correctly classified images. + :param data_name: Name of the dataset (e.g., 'mnist', 'cifar10'). + :param model_name: Name of the model to evaluate. + :param batch_size: Batch size for evaluation. + :return: List of indices of correctly classified images. + """ + # Set TF random seed for reproducibility + tf.set_random_seed(1234) + + # Create TF session + config = tf.ConfigProto() + config.gpu_options.allow_growth = True + sess = tf.Session(config=config) + print("Created TensorFlow session.") + #set_log_level(logging.DEBUG) + + # Load dataset + if data_name == 'mnist': + from cleverhans.utils_mnist import data_mnist + X_train, Y_train, X_test, Y_test = data_mnist(train_start=0, train_end=60000, + test_start=0, test_end=10000) + elif data_name in ['cifar10', 'plane_frog']: + from import_data_cifar10 import load_data_cifar10 + labels = None + if data_name == 'plane_frog': + labels = [0, 6] + datapath = '../cifar_data/' + X_train, X_test, Y_train, Y_test = load_data_cifar10(datapath, labels=labels) + else: + raise ValueError("Unsupported dataset: {}".format(data_name)) + + img_rows, img_cols, channels = X_test[0].shape + nb_classes = Y_test.shape[1] + + # Define placeholders + x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels)) + y = tf.placeholder(tf.float32, shape=(None, nb_classes)) + + # Load model + model = load_classifier(sess, model_name, data_name) + + # Ensure correct learning phase for evaluation + if 'bnn' not in model_name: + keras.backend.set_learning_phase(0) + else: + keras.backend.set_learning_phase(1) + + # Model predictions + preds = model.predict(x, softmax=False) + + # Evaluate accuracy and retrieve predictions + eval_params = {'batch_size': batch_size} + accuracy, y_pred_clean = model_eval(sess, x, y, preds, X_test, Y_test, + args=eval_params, return_pred=True) + print('Test accuracy on legitimate test examples: {:.2f}%'.format(accuracy * 100)) + + # Extract indices of correctly classified images + correct_prediction = (np.argmax(Y_test, axis=1) == np.argmax(y_pred_clean, axis=1)) + correct_indices = np.where(correct_prediction)[0] + print('Number of correctly classified images: {}/{}'.format(len(correct_indices), len(X_test))) + + # Save indices + output_dir = 'correct_indices' + if not os.path.exists(output_dir): + os.makedirs(output_dir) + output_file = os.path.join(output_dir, f'{data_name}_{model_name}_correct_indices.pkl') + with open(output_file, 'wb') as f: + pickle.dump(correct_indices, f) + print(f"Correct indices saved to {output_file}") + + return correct_indices + + +if __name__ == '__main__': + # Define command-line flags + flags.DEFINE_string('data_name', 'mnist', 'Dataset name (e.g., mnist, cifar10)') + flags.DEFINE_string('model_name', 'bayes_K10_A', 'Model name to evaluate') + flags.DEFINE_integer('batch_size', 128, 'Batch size for evaluation') + + # Parse flags and execute + args = FLAGS + extract_correct_indices(data_name=args.data_name, model_name=args.model_name, batch_size=args.batch_size) diff --git a/test_attacks/detect_attacks_logp.py b/test_attacks/detect_attacks_logp.py index 463489e..1ccb935 100644 --- a/test_attacks/detect_attacks_logp.py +++ b/test_attacks/detect_attacks_logp.py @@ -10,7 +10,9 @@ import os, sys, pickle, argparse sys.path.append('../utils/') from model_eval import model_eval -from scipy.misc import logsumexp +#from scipy.misc import logsumexp +from scipy.special import logsumexp + import keras.backend sys.path.append('load/') from load_classifier import load_classifier diff --git a/test_attacks/load/__pycache__/load_bayes_classifier.cpython-36.pyc b/test_attacks/load/__pycache__/load_bayes_classifier.cpython-36.pyc new file mode 100644 index 0000000..b6317b6 Binary files /dev/null and b/test_attacks/load/__pycache__/load_bayes_classifier.cpython-36.pyc differ diff --git a/test_attacks/load/__pycache__/load_classifier.cpython-36.pyc b/test_attacks/load/__pycache__/load_classifier.cpython-36.pyc new file mode 100644 index 0000000..536028d Binary files /dev/null and b/test_attacks/load/__pycache__/load_classifier.cpython-36.pyc differ diff --git a/test_attacks/load/load_bayes_classifier.py b/test_attacks/load/load_bayes_classifier.py index fe34213..1179d02 100644 --- a/test_attacks/load/load_bayes_classifier.py +++ b/test_attacks/load/load_bayes_classifier.py @@ -94,15 +94,27 @@ def bayes_classifier(x, enc, dec, ll, dimY, dimZ, lowerbound, K = 1, beta=1.0, u if use_mean: K=1 enc_conv, enc_mlp = enc fea = enc_conv(x) - N = x.get_shape().as_list()[0] + N = x.get_shape().as_list()[0] + #N = tf.shape(x)[0] # Récupère la taille dynamique du batch + logpxy = [] if no_z: z_holder = tf.zeros([N, dimZ]) K = 1 else: z_holder = None + + print(f"N: {N}, dimY: {dimY}") + assert N is not None, "N (batch size) is None. Check how it is being computed." + assert dimY is not None, "dimY (number of classes) is None. Ensure it is set correctly." + for i in range(dimY): y = np.zeros([N, dimY]); y[:, i] = 1; y = tf.constant(np.asarray(y, dtype='f')) + + #y = tf.one_hot(indices=i, depth=dimY, on_value=1.0, off_value=0.0) + #y = tf.tile(tf.expand_dims(y, axis=0), [N, 1]) # Répète le vecteur pour chaque exemple du batch + + bound = lowerbound(x, fea, y, enc_mlp, dec, ll, K, IS=False, beta=beta, use_mean=use_mean, fix_samples=fix_samples, seed=seed, z=z_holder) logpxy.append(tf.expand_dims(bound, 1)) diff --git a/test_attacks/mosaique.py b/test_attacks/mosaique.py new file mode 100644 index 0000000..db6fc64 --- /dev/null +++ b/test_attacks/mosaique.py @@ -0,0 +1,91 @@ +import pickle +import matplotlib.pyplot as plt +import numpy as np +import os + +# Charger les résultats sauvegardés +path_to_results = 'raw_attack_results/bayes_K10_A_cnn/mnist_fgsm_eps0.10_untargeted.pkl' +output_dir = 'adversarial_images' + +# Créer le dossier de sortie +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +with open(path_to_results, 'rb') as f: + adv, true_ys, adv_ys, adv_logits = pickle.load(f) + +# Charger les données originales (vous devez fournir X_test) +# Remplacez cette ligne par le chemin vers vos données originales si nécessaire +from cleverhans.utils_mnist import data_mnist +_, _, X_test, _ = data_mnist(train_start=0, train_end=60000, test_start=0, test_end=10000) + +# Noms des catégories pour Fashion MNIST +LABEL_NAMES = [ + "T-shirt/top", "Trouser", "Pullover", "Dress", "Coat", + "Sandal", "Shirt", "Sneaker", "Bag", "Ankle boot" +] + +# Identifier les prédictions et les labels +adv_preds = np.argmax(adv_logits, axis=1) +true_labels = np.argmax(true_ys, axis=1) + +# Mélanger les indices pour avoir une mosaïque variée +indices = np.arange(len(adv)) +np.random.shuffle(indices) + +# Extraire les informations du chemin pour générer le titre +attack_type = path_to_results.split('/')[-1].split('_')[1] # fgsm +eps_value = path_to_results.split('_')[3].replace("eps", "") # 0.10 +title_text = f"A model's classification with {attack_type.upper()} eps {eps_value} attacks." + +# Paramètres de la mosaïque +rows, cols = 5, 5 +fig, axes = plt.subplots(rows * 2, cols, figsize=(12, 20)) # Double hauteur pour deux grilles + +# Boucle pour afficher les images attaquées (première mosaïque) +for i, ax in zip(indices[:rows * cols], axes[:rows].flatten()): + ax.imshow(adv[i].reshape(28, 28), cmap='gray') + + # Annoter avec les noms des catégories + true_name = LABEL_NAMES[true_labels[i]] + pred_name = LABEL_NAMES[adv_preds[i]] + + color = 'red' if adv_preds[i] != true_labels[i] else 'green' + ax.set_title(f"True: {true_name}\nPred: {pred_name}", fontsize=8) + ax.spines['top'].set_color(color) + ax.spines['bottom'].set_color(color) + ax.spines['left'].set_color(color) + ax.spines['right'].set_color(color) + ax.spines['top'].set_linewidth(2) + ax.spines['bottom'].set_linewidth(2) + ax.spines['left'].set_linewidth(2) + ax.spines['right'].set_linewidth(2) + ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False) + +# Boucle pour afficher les images originales (deuxième mosaïque) +for i, ax in zip(indices[:rows * cols], axes[rows:].flatten()): + ax.imshow(X_test[i].reshape(28, 28), cmap='gray') # Image originale + + # Annoter avec le nom de la catégorie réelle + true_name = LABEL_NAMES[true_labels[i]] + ax.set_title(f"True: {true_name}", fontsize=8) + ax.spines['top'].set_color('blue') + ax.spines['bottom'].set_color('blue') + ax.spines['left'].set_color('blue') + ax.spines['right'].set_color('blue') + ax.spines['top'].set_linewidth(2) + ax.spines['bottom'].set_linewidth(2) + ax.spines['left'].set_linewidth(2) + ax.spines['right'].set_linewidth(2) + ax.tick_params(left=False, bottom=False, labelleft=False, labelbottom=False) + +# Ajouter un titre global +fig.suptitle(title_text, fontsize=16) +plt.tight_layout(rect=[0, 0, 1, 0.96]) # Réserver de l'espace pour le titre + +# Sauvegarder la mosaïque +output_path = os.path.join(output_dir, "mosaic_results_with_originals.png") +plt.savefig(output_path, dpi=150) +plt.close() + +print(f"Mosaïque avec labels sauvegardée dans {output_path}") diff --git a/test_attacks/raw_attack_results/bayes_K10_A_cnn/mnist_fgsm_eps0.10_untargeted.pkl b/test_attacks/raw_attack_results/bayes_K10_A_cnn/mnist_fgsm_eps0.10_untargeted.pkl new file mode 100644 index 0000000..ba34b03 Binary files /dev/null and b/test_attacks/raw_attack_results/bayes_K10_A_cnn/mnist_fgsm_eps0.10_untargeted.pkl differ diff --git a/test_attacks/save_miss_classified.py b/test_attacks/save_miss_classified.py new file mode 100644 index 0000000..288e238 --- /dev/null +++ b/test_attacks/save_miss_classified.py @@ -0,0 +1,44 @@ +import pickle +import os +import matplotlib.pyplot as plt +import numpy as np + +# Charger les résultats sauvegardés +path_to_results = 'raw_attack_results/bayes_K10_A_cnn/mnist_fgsm_eps0.10_untargeted.pkl' +output_dir = 'adversarial_images' + +# Créer le dossier de sortie +if not os.path.exists(output_dir): + os.makedirs(output_dir) + +# Charger les résultats +with open(path_to_results, 'rb') as f: + adv, true_ys, adv_ys, adv_logits = pickle.load(f) + +# Identifier les exemples mal classifiés +adv_preds = np.argmax(adv_logits, axis=1) +true_labels = np.argmax(true_ys, axis=1) +misclassified_indices = np.where(adv_preds != true_labels)[0] + +print(f"Nombre d'exemples mal classifiés : {len(misclassified_indices)}") + +# Enregistrer les images mal classifiées +for idx, i in enumerate(misclassified_indices): + plt.imshow(adv[i].reshape(28, 28), cmap='gray') + plt.title(f"True: {true_labels[i]}, Predicted: {adv_preds[i]}") + plt.axis('off') + output_path = os.path.join(output_dir, f"misclassified_{idx}.png") + plt.savefig(output_path) + plt.close() + + +html_output_path = os.path.join(output_dir, "report.html") + +with open(html_output_path, "w") as f: + f.write("\n") + f.write("

Rapport des exemples mal classifiés

\n") + for idx, i in enumerate(misclassified_indices): + image_path = f"misclassified_{idx}.png" + f.write(f"

True: {true_labels[i]}, Predicted: {adv_preds[i]}

\n") + f.write(f"

\n") + f.write("\n") diff --git a/utils/__pycache__/utils.cpython-312.pyc b/utils/__pycache__/utils.cpython-312.pyc new file mode 100644 index 0000000..2f92e5b Binary files /dev/null and b/utils/__pycache__/utils.cpython-312.pyc differ diff --git a/utils/__pycache__/utils.cpython-36.pyc b/utils/__pycache__/utils.cpython-36.pyc new file mode 100644 index 0000000..cf09e33 Binary files /dev/null and b/utils/__pycache__/utils.cpython-36.pyc differ diff --git a/utils/__pycache__/utils_mnist.cpython-36.pyc b/utils/__pycache__/utils_mnist.cpython-36.pyc new file mode 100644 index 0000000..91b17ba Binary files /dev/null and b/utils/__pycache__/utils_mnist.cpython-36.pyc differ diff --git a/utils/__pycache__/visualisation.cpython-312.pyc b/utils/__pycache__/visualisation.cpython-312.pyc new file mode 100644 index 0000000..8a38826 Binary files /dev/null and b/utils/__pycache__/visualisation.cpython-312.pyc differ diff --git a/utils/__pycache__/visualisation.cpython-36.pyc b/utils/__pycache__/visualisation.cpython-36.pyc new file mode 100644 index 0000000..aa9eafb Binary files /dev/null and b/utils/__pycache__/visualisation.cpython-36.pyc differ diff --git a/utils/utils_mnist.py b/utils/utils_mnist.py index 8cfdf5c..8339989 100644 --- a/utils/utils_mnist.py +++ b/utils/utils_mnist.py @@ -24,14 +24,39 @@ def data_mnist(datadir='/tmp/', train_start=0, train_end=60000, test_start=0, assert isinstance(train_end, int) assert isinstance(test_start, int) assert isinstance(test_end, int) - + ''' old from tensorflow.examples.tutorials.mnist import input_data + mnist = input_data.read_data_sets(datadir, one_hot=True, reshape=False) X_train = np.vstack((mnist.train.images, mnist.validation.images)) Y_train = np.vstack((mnist.train.labels, mnist.validation.labels)) X_test = mnist.test.images Y_test = mnist.test.labels + X_train = X_train[train_start:train_end] + Y_train = Y_train[train_start:train_end] + X_test = X_test[test_start:test_end] + Y_test = Y_test[test_start:test_end]''' + + from keras.datasets import fashion_mnist + + # Load data from keras + (X_train, Y_train), (X_test, Y_test) = fashion_mnist.load_data() + + # Normalize data to [0, 1] range + X_train = X_train.astype('float32') / 255.0 + X_test = X_test.astype('float32') / 255.0 + + # Add channel dimension (28x28 -> 28x28x1) + X_train = np.expand_dims(X_train, axis=-1) + X_test = np.expand_dims(X_test, axis=-1) + + # One-hot encode labels + from keras.utils import to_categorical + Y_train = to_categorical(Y_train, 10) + Y_test = to_categorical(Y_test, 10) + + # Apply slicing X_train = X_train[train_start:train_end] Y_train = Y_train[train_start:train_end] X_test = X_test[test_start:test_end] diff --git a/vae_cifar_fea.py b/vae_cifar_fea.py index 4d31d31..3b6db9d 100755 --- a/vae_cifar_fea.py +++ b/vae_cifar_fea.py @@ -8,7 +8,7 @@ from visualisation import plot_images from vae_new import construct_optimizer -n_iter = 100 +n_iter = 20 batch_size = 50 lr = 1e-4#5e-5 K = 1 diff --git a/vae_mnist.py b/vae_mnist.py index f1fffdb..f8de769 100644 --- a/vae_mnist.py +++ b/vae_mnist.py @@ -10,7 +10,7 @@ dimZ = 64 dimH = 500 -n_iter = 100 +n_iter = 20 #100 batch_size = 50 lr = 1e-4 K = 1 @@ -57,14 +57,28 @@ def main(data_name, vae_type, dimZ, dimH, n_iter, batch_size, K, checkpoint): config = tf.ConfigProto() config.gpu_options.allow_growth = True sess = tf.Session(config=config) - if not os.path.isdir('save/'): + + '''if not os.path.isdir('save/'): os.mkdir('save/') print('create path save/') path_name = data_name + '_conv_vae_%s/' % (vae_type + '_' + str(dimZ)) if not os.path.isdir('save/'+path_name): os.mkdir('save/'+path_name) print('create path save/' + path_name) - filename = 'save/' + path_name + 'checkpoint' + filename = 'save/' + path_name + 'checkpoint' ''' + + # Chemin de base pour les sauvegardes + save_dir = '/app/save/' + + if not os.path.isdir(save_dir): + os.mkdir(save_dir) + path_name = data_name + '_conv_vae_%s/' % (vae_type + '_' + str(dimZ)) + if not os.path.isdir(save_dir + path_name): + os.mkdir(save_dir + path_name) + filename = save_dir + path_name + 'checkpoint' + + + if checkpoint < 0: print('training from scratch') init_variables(sess) @@ -74,10 +88,15 @@ def main(data_name, vae_type, dimZ, dimH, n_iter, batch_size, K, checkpoint): # now start fitting n_iter_ = min(n_iter,20) + print("on va commencer le train ", n_iter, n_iter_) + print("nombre iterations ", int(n_iter/n_iter_)) + beta = 1.0 for i in range(int(n_iter/n_iter_)): + print("fit", i) fit(sess, X_train, Y_train, n_iter_, lr, beta) # print training and test accuracy + print("eval", i) eval_acc(sess, X_test, Y_test, 'test') # save param values