BoghdadyJR/codesearch_python · Data Studio

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def compute_gradient(model_fn, x, y, targeted): """ Computes the gradient of the loss with respect to the input tensor. :param model_fn: a callable that takes an input tensor and returns the model logits. :param x: input tensor :param y: Tensor with true labels. If targeted is true, then provide the target la...
def optimize_linear(grad, eps, ord=np.inf): """ Solves for the optimal input to a linear function under a norm constraint. Optimal_perturbation = argmax_{eta, \|\|eta\|\|_{ord} < eps} dot(eta, grad) :param grad: tf tensor containing a batch of gradients :param eps: float scalar specifying size of constraint reg...
def save_pdf(path): """ Saves a pdf of the current matplotlib figure. :param path: str, filepath to save to """ pp = PdfPages(path) pp.savefig(pyplot.gcf()) pp.close()
def clip_eta(eta, ord, eps): """ Helper function to clip the perturbation to epsilon norm ball. :param eta: A tensor with the current perturbation. :param ord: Order of the norm (mimics Numpy). Possible values: np.inf, 1 or 2. :param eps: Epsilon, bound of the perturbation. """ # Clipping p...
def prep_bbox(sess, x, y, x_train, y_train, x_test, y_test, nb_epochs, batch_size, learning_rate, rng, nb_classes=10, img_rows=28, img_cols=28, nchannels=1): """ Define and train a model that simulates the "remote" black-box oracle described in the original paper. :param sess: the TF...
def train_sub(sess, x, y, bbox_preds, x_sub, y_sub, nb_classes, nb_epochs_s, batch_size, learning_rate, data_aug, lmbda, aug_batch_size, rng, img_rows=28, img_cols=28, nchannels=1): """ This function creates the substitute by alternatively augmenting the training data and...
def mnist_blackbox(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_classes=NB_CLASSES, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, nb_epochs=NB_EPOCHS, holdout=HOLDOUT, data_aug=DATA_AUG, nb_epochs_s=NB_EPOCHS_S, lmbda=...
def random_shift(x, pad=(4, 4), mode='REFLECT'): """Pad a single image and then crop to the original size with a random offset.""" assert mode in 'REFLECT SYMMETRIC CONSTANT'.split() assert x.get_shape().ndims == 3 xp = tf.pad(x, [[pad[0], pad[0]], [pad[1], pad[1]], [0, 0]], mode) return tf.random_crop(xp, ...
def batch_augment(x, func, device='/CPU:0'): """ Apply dataset augmentation to a batch of exmaples. :param x: Tensor representing a batch of examples. :param func: Callable implementing dataset augmentation, operating on a single image. :param device: String specifying which device to use. """ with tf...
def random_crop_and_flip(x, pad_rows=4, pad_cols=4): """Augment a batch by randomly cropping and horizontally flipping it.""" rows = tf.shape(x)[1] cols = tf.shape(x)[2] channels = x.get_shape()[3] def _rand_crop_img(img): """Randomly crop an individual image""" return tf.random_crop(img, [rows, cols...
def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, clean_train=CLEAN_TRAIN, testing=False, backprop_through_attack=BACKPRO...
def _project_perturbation(perturbation, epsilon, input_image, clip_min=None, clip_max=None): """Project `perturbation` onto L-infinity ball of radius `epsilon`. Also project into hypercube such that the resulting adversarial example is between clip_min and clip_max, if applicable. """ ...
def margin_logit_loss(model_logits, label, nb_classes=10, num_classes=None): """Computes difference between logit for `label` and next highest logit. The loss is high when `label` is unlikely (targeted by default). This follows the same interface as `loss_fn` for TensorOptimizer and projected_optimization, i.e...
def spm(x, model, y=None, n_samples=None, dx_min=-0.1, dx_max=0.1, n_dxs=5, dy_min=-0.1, dy_max=0.1, n_dys=5, angle_min=-30, angle_max=30, n_angles=31, black_border_size=0): """ TensorFlow implementation of the Spatial Transformation Method. :return: a tensor for the adversarial example """ if...
def parallel_apply_transformations(x, transforms, black_border_size=0): """ Apply image transformations in parallel. :param transforms: TODO :param black_border_size: int, size of black border to apply Returns: Transformed images """ transforms = tf.convert_to_tensor(transforms, dtype=tf.float32) x ...
def projected_optimization(loss_fn, input_image, label, epsilon, num_steps, clip_min=None, clip_max=None, optimizer=TensorAdam(), ...
def generate(self, x, y=None, y_target=None, eps=None, clip_min=None, clip_max=None, nb_iter=None, is_targeted=None, early_stop_loss_threshold=None, learning_rate=DEFAULT...
def _compute_gradients(self, loss_fn, x, unused_optim_state): """Compute a new value of `x` to minimize `loss_fn`. Args: loss_fn: a callable that takes `x`, a batch of images, and returns a batch of loss values. `x` will be optimized to minimize `loss_fn(x)`. x: A list o...
def minimize(self, loss_fn, x, optim_state): """ Analogous to tf.Optimizer.minimize :param loss_fn: tf Tensor, representing the loss to minimize :param x: list of Tensor, analogous to tf.Optimizer's var_list :param optim_state: A possibly nested dict, containing any optimizer state. Returns: ...
def init_state(self, x): """ Initialize t, m, and u """ optim_state = {} optim_state["t"] = 0. optim_state["m"] = [tf.zeros_like(v) for v in x] optim_state["u"] = [tf.zeros_like(v) for v in x] return optim_state
def _apply_gradients(self, grads, x, optim_state): """Refer to parent class documentation.""" new_x = [None] * len(x) new_optim_state = { "t": optim_state["t"] + 1., "m": [None] * len(x), "u": [None] * len(x) } t = new_optim_state["t"] for i in xrange(len(x)): g = g...
def _compute_gradients(self, loss_fn, x, unused_optim_state): """Compute gradient estimates using SPSA.""" # Assumes `x` is a list, containing a [1, H, W, C] image # If static batch dimension is None, tf.reshape to batch size 1 # so that static shape can be inferred assert len(x) == 1 static_x_s...
def parse_args(): """Parses command line arguments.""" parser = argparse.ArgumentParser( description='Tool to run attacks and defenses.') parser.add_argument('--attacks_dir', required=True, help='Location of all attacks.') parser.add_argument('--targeted_attacks_dir', required=True, ...
def read_submissions_from_directory(dirname, use_gpu): """Scans directory and read all submissions. Args: dirname: directory to scan. use_gpu: whether submissions should use GPU. This argument is used to pick proper Docker container for each submission and create instance of Attack or Defense c...
def load_defense_output(filename): """Loads output of defense from given file.""" result = {} with open(filename) as f: for row in csv.reader(f): try: image_filename = row[0] if image_filename.endswith('.png') or image_filename.endswith('.jpg'): image_filename = image_filename[...
def compute_and_save_scores_and_ranking(attacks_output, defenses_output, dataset_meta, output_dir, save_all_classification=False): """Computes scores and rank...
def main(): """Run all attacks against all defenses and compute results. """ args = parse_args() attacks_output_dir = os.path.join(args.intermediate_results_dir, 'attacks_output') targeted_attacks_output_dir = os.path.join(args.intermediate_results_dir, ...
def run(self, input_dir, output_dir, epsilon): """Runs attack inside Docker. Args: input_dir: directory with input (dataset). output_dir: directory where output (adversarial images) should be written. epsilon: maximum allowed size of adversarial perturbation, should be in range [0, 25...
def _load_dataset_clipping(self, dataset_dir, epsilon): """Helper method which loads dataset and determines clipping range. Args: dataset_dir: location of the dataset. epsilon: maximum allowed size of adversarial perturbation. """ self.dataset_max_clip = {} self.dataset_min_clip = {} ...
def clip_and_copy_attack_outputs(self, attack_name, is_targeted): """Clips results of attack and copy it to directory with all images. Args: attack_name: name of the attack. is_targeted: if True then attack is targeted, otherwise non-targeted. """ if is_targeted: self._targeted_attack...
def save_target_classes(self, filename): """Saves target classed for all dataset images into given file.""" with open(filename, 'w') as f: for k, v in self._target_classes.items(): f.write('{0}.png,{1}\n'.format(k, v))
def single_run_max_confidence_recipe(sess, model, x, y, nb_classes, eps, clip_min, clip_max, eps_iter, nb_iter, report_path, batch_size=BATCH_SIZE, eps_iter_small=None): ...
def random_search_max_confidence_recipe(sess, model, x, y, eps, clip_min, clip_max, report_path, batch_size=BATCH_SIZE, num_noise_points=10000): """Max confidence using random search. References:...
def bundle_attacks(sess, model, x, y, attack_configs, goals, report_path, attack_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE): """ Runs attack bundling. Users of cleverhans may call this function but are more likely to call one of the recipes above. Reference: https://openreview.net/...
def bundle_attacks_with_goal(sess, model, x, y, adv_x, attack_configs, run_counts, goal, report, report_path, attack_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE): """ Runs attack bundling, working on one specific AttackGoal...
def run_batch_with_goal(sess, model, x, y, adv_x_val, criteria, attack_configs, run_counts, goal, report, report_path, attack_batch_size=BATCH_SIZE): """ Runs attack bundling on one batch of data. This function is mostly intended to be called by `bundle_attacks_wi...
def save(criteria, report, report_path, adv_x_val): """ Saves the report and adversarial examples. :param criteria: dict, of the form returned by AttackGoal.get_criteria :param report: dict containing a confidence report :param report_path: string, filepath :param adv_x_val: numpy array containing dataset o...
def unfinished_attack_configs(new_work_goal, work_before, run_counts, log=False): """ Returns a list of attack configs that have not yet been run the desired number of times. :param new_work_goal: dict mapping attacks to desired number of times to run :param work_before: dict map...
def bundle_examples_with_goal(sess, model, adv_x_list, y, goal, report_path, batch_size=BATCH_SIZE): """ A post-processor version of attack bundling, that chooses the strongest example from the output of multiple earlier bundling strategies. :param sess: tf.session.Session :para...
def spsa_max_confidence_recipe(sess, model, x, y, nb_classes, eps, clip_min, clip_max, nb_iter, report_path, spsa_samples=SPSA.DEFAULT_SPSA_SAMPLES, spsa_iters=SPSA.DEFAULT_SPSA_ITERS, ...
def get_criteria(self, sess, model, advx, y, batch_size=BATCH_SIZE): """ Returns a dictionary mapping the name of each criterion to a NumPy array containing the value of that criterion for each adversarial example. Subclasses can add extra criteria by implementing the `extra_criteria` method. ...
def request_examples(self, attack_config, criteria, run_counts, batch_size): """ Returns a numpy array of integer example indices to run in the next batch. """ raise NotImplementedError(str(type(self)) + "needs to implement request_examples")
def new_wins(self, orig_criteria, orig_idx, new_criteria, new_idx): """ Returns a bool indicating whether a new adversarial example is better than the pre-existing one for the same clean example. :param orig_criteria: dict mapping names of criteria to their value for each example in the whole data...
def filter(self, run_counts, criteria): """ Return run counts only for examples that are still correctly classified """ correctness = criteria['correctness'] assert correctness.dtype == np.bool filtered_counts = deep_copy(run_counts) for key in filtered_counts: filtered_counts[key] = f...
def filter(self, run_counts, criteria): """ Return the counts for only those examples that are below the threshold """ wrong_confidence = criteria['wrong_confidence'] below_t = wrong_confidence <= self.t filtered_counts = deep_copy(run_counts) for key in filtered_counts: filtered_count...
def projected_gradient_descent(model_fn, x, eps, eps_iter, nb_iter, ord, clip_min=None, clip_max=None, y=None, targeted=False, rand_init=None, rand_minmax=0.3, sanity_checks=True): """ This class implements either the Basic Iterative Method (Kurakin et...
def clip_image(image, clip_min, clip_max): """ Clip an image, or an image batch, with upper and lower threshold. """ return np.minimum(np.maximum(clip_min, image), clip_max)
def compute_distance(x_ori, x_pert, constraint='l2'): """ Compute the distance between two images. """ if constraint == 'l2': dist = np.linalg.norm(x_ori - x_pert) elif constraint == 'linf': dist = np.max(abs(x_ori - x_pert)) return dist
def approximate_gradient(decision_function, sample, num_evals, delta, constraint, shape, clip_min, clip_max): """ Gradient direction estimation """ # Generate random vectors. noise_shape = [num_evals] + list(shape) if constraint == 'l2': rv = np.random.randn(*noise_shape) elif con...
def project(original_image, perturbed_images, alphas, shape, constraint): """ Projection onto given l2 / linf balls in a batch. """ alphas_shape = [len(alphas)] + [1] * len(shape) alphas = alphas.reshape(alphas_shape) if constraint == 'l2': projected = (1-alphas) * original_image + alphas * perturbed_images...
def binary_search_batch(original_image, perturbed_images, decision_function, shape, constraint, theta): """ Binary search to approach the boundary. """ # Compute distance between each of perturbed image and original image. dists_post_update = np.array([ compute_distance( o...
def initialize(decision_function, sample, shape, clip_min, clip_max): """ Efficient Implementation of BlendedUniformNoiseAttack in Foolbox. """ success = 0 num_evals = 0 # Find a misclassified random noise. while True: random_noise = np.random.uniform(clip_min, clip_max, size=shape) success = dec...
def geometric_progression_for_stepsize(x, update, dist, decision_function, current_iteration): """ Geometric progression to search for stepsize. Keep decreasing stepsize by half until reaching the desired side of the boundary. """ epsilon = dist / np.sqrt(current...
def select_delta(dist_post_update, current_iteration, clip_max, clip_min, d, theta, constraint): """ Choose the delta at the scale of distance between x and perturbed sample. """ if current_iteration == 1: delta = 0.1 * (clip_max - clip_min) else: if constraint == 'l2': delta...
def generate(self, x, kwargs): """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: A tensor with the inputs. :param kwargs: See `parse_params` """ self.parse_params(kwargs) shape = [int(i) ...
def generate_np(self, x, kwargs): """ Generate adversarial images in a for loop. :param y: An array of shape (n, nb_classes) for true labels. :param y_target: An array of shape (n, nb_classes) for target labels. Required for targeted attack. :param image_target: An array of shape (n, image ...
def parse_params(self, y_target=None, image_target=None, initial_num_evals=100, max_num_evals=10000, stepsize_search='grid_search', num_iterations=64, gamma=0.01, const...
def _bapp(self, sample, target_label, target_image): """ Main algorithm for Boundary Attack ++. Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param sample: input image. Without the batchsize dimension. :par...
def parse_params(self, layer=None, eps=0.3, eps_iter=0.05, nb_iter=10, ord=np.inf, clip_min=None, clip_max=None, **kwargs): """ Take in a dictionary of paramete...
def attack_single_step(self, x, eta, g_feat): """ TensorFlow implementation of the Fast Feature Gradient. This is a single step attack similar to Fast Gradient Method that attacks an internal representation. :param x: the input placeholder :param eta: A tensor the same shape as x that holds the...
def generate(self, x, g, **kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param g: The target value of the symbolic representation :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert...
def main(argv=None): """ Make a confidence report and save it to disk. """ try: _name_of_script, filepath = argv except ValueError: raise ValueError(argv) print(filepath) make_confidence_report_bundled(filepath=filepath, test_start=FLAGS.test_start, ...
def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): """Builds the 35x35 resnet block.""" with tf.variable_scope(scope, 'Block35', [net], reuse=reuse): with tf.variable_scope('Branch_0'): tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1') with tf.variable_scope('Branch_...
def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): """Builds the 17x17 resnet block.""" with tf.variable_scope(scope, 'Block17', [net], reuse=reuse): with tf.variable_scope('Branch_0'): tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') with tf.variable_scope('Branch...
def inception_resnet_v2_base(inputs, final_endpoint='Conv2d_7b_1x1', output_stride=16, align_feature_maps=False, scope=None): """Inception model from http://arxiv.org/abs/1602.07261. Constructs an I...
def inception_resnet_v2(inputs, nb_classes=1001, is_training=True, dropout_keep_prob=0.8, reuse=None, scope='InceptionResnetV2', create_aux_logits=True, num_classes=None): """Creates the Inception R...
def inception_resnet_v2_arg_scope(weight_decay=0.00004, batch_norm_decay=0.9997, batch_norm_epsilon=0.001): """Returns the scope with the default parameters for inception_resnet_v2. Args: weight_decay: the weight decay for weights variables. ...
def ld_mnist(): """Load training and test data.""" def convert_types(image, label): image = tf.cast(image, tf.float32) image /= 255 return image, label dataset, info = tfds.load('mnist', data_dir='gs://tfds-data/datasets', with_info=True, as_supervised=True) mnist_train...
def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, testing=False, label_smoothing=0.1): """ MNIST CleverHans tutorial :param train_start: index of first t...
def main(args): """Validate all submissions and copy them into place""" random.seed() temp_dir = tempfile.mkdtemp() logging.info('Created temporary directory: %s', temp_dir) validator = SubmissionValidator( source_dir=args.source_dir, target_dir=args.target_dir, temp_dir=temp_dir, do_c...
def _update_stat(self, submission_type, increase_success, increase_fail): """Common method to update submission statistics.""" stat = self.stats.get(submission_type, (0, 0)) stat = (stat[0] + increase_success, stat[1] + increase_fail) self.stats[submission_type] = stat
def log_stats(self): """Print statistics into log.""" logging.info('Validation statistics: ') for k, v in iteritems(self.stats): logging.info('%s - %d valid out of %d total submissions', k, v[0], v[0] + v[1])
def copy_submission_locally(self, cloud_path): """Copies submission from Google Cloud Storage to local directory. Args: cloud_path: path of the submission in Google Cloud Storage Returns: name of the local file where submission is copied to """ local_path = os.path.join(self.download_d...
def copy_submission_to_destination(self, src_filename, dst_subdir, submission_id): """Copies submission to target directory. Args: src_filename: source filename of the submission dst_subdir: subdirectory of the target directory where submission should be...
def validate_and_copy_one_submission(self, submission_path): """Validates one submission and copies it to target directory. Args: submission_path: path in Google Cloud Storage of the submission file """ if os.path.exists(self.download_dir): shutil.rmtree(self.download_dir) os.makedirs(s...
def save_id_to_path_mapping(self): """Saves mapping from submission IDs to original filenames. This mapping is saved as CSV file into target directory. """ if not self.id_to_path_mapping: return with open(self.local_id_to_path_mapping_file, 'w') as f: writer = csv.writer(f) writer...
def run(self): """Runs validation of all submissions.""" cmd = ['gsutil', 'ls', os.path.join(self.source_dir, '**')] try: files_list = subprocess.check_output(cmd).split('\n') except subprocess.CalledProcessError: logging.error('Can''t read source directory') all_submissions = [ ...
def main(argv=None): """Takes the path to a directory with reports and renders success fail plots.""" report_paths = argv[1:] fail_names = FLAGS.fail_names.split(',') for report_path in report_paths: plot_report_from_path(report_path, label=report_path, fail_names=fail_names) pyplot.legend() pyplot.x...
def is_unclaimed(work): """Returns True if work piece is unclaimed.""" if work['is_completed']: return False cutoff_time = time.time() - MAX_PROCESSING_TIME if (work['claimed_worker_id'] and work['claimed_worker_start_time'] is not None and work['claimed_worker_start_time'] >= cutoff_time): ...
def write_all_to_datastore(self): """Writes all work pieces into datastore. Each work piece is identified by ID. This method writes/updates only those work pieces which IDs are stored in this class. For examples, if this class has only work pieces with IDs '1' ... '100' and datastore already contains ...
def read_all_from_datastore(self): """Reads all work pieces from the datastore.""" self._work = {} client = self._datastore_client parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id) for entity in client.query_fetch(kind=KIND_WORK, ancestor=parent_key): work_id = entity.key.flat...
def _read_undone_shard_from_datastore(self, shard_id=None): """Reads undone worke pieces which are assigned to shard with given id.""" self._work = {} client = self._datastore_client parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id) filters = [('is_completed', '=', False)] if sh...
def read_undone_from_datastore(self, shard_id=None, num_shards=None): """Reads undone work from the datastore. If shard_id and num_shards are specified then this method will attempt to read undone work for shard with id shard_id. If no undone work was found then it will try to read shard (shard_id+1) a...
def try_pick_piece_of_work(self, worker_id, submission_id=None): """Tries pick next unclaimed piece of work to do. Attempt to claim work piece is done using Cloud Datastore transaction, so only one worker can claim any work piece at a time. Args: worker_id: ID of current worker submission_...
def update_work_as_completed(self, worker_id, work_id, other_values=None, error=None): """Updates work piece in datastore as completed. Args: worker_id: ID of the worker which did the work work_id: ID of the work which was done other_values: dictionary with addi...
def compute_work_statistics(self): """Computes statistics from all work pieces stored in this class.""" result = {} for v in itervalues(self.work): submission_id = v['submission_id'] if submission_id not in result: result[submission_id] = { 'completed': 0, 'num_er...
def init_from_adversarial_batches(self, adv_batches): """Initializes work pieces from adversarial batches. Args: adv_batches: dict with adversarial batches, could be obtained as AversarialBatches.data """ for idx, (adv_batch_id, adv_batch_val) in enumerate(iteritems(adv_batches)): w...
def init_from_class_batches(self, class_batches, num_shards=None): """Initializes work pieces from classification batches. Args: class_batches: dict with classification batches, could be obtained as ClassificationBatches.data num_shards: number of shards to split data into, if None ...
def main(argv=None): """ Make a confidence report and save it to disk. """ assert len(argv) >= 3 _name_of_script = argv[0] model_filepath = argv[1] adv_x_filepaths = argv[2:] sess = tf.Session() with sess.as_default(): model = serial.load(model_filepath) factory = model.dataset_factory facto...
def fgm(x, logits, y=None, eps=0.3, ord=np.inf, clip_min=None, clip_max=None, targeted=False, sanity_checks=True): """ TensorFlow implementation of the Fast Gradient Method. :param x: the input placeholder :param logits: output of model.get_logits ...
def optimize_linear(grad, eps, ord=np.inf): """ Solves for the optimal input to a linear function under a norm constraint. Optimal_perturbation = argmax_{eta, \|\|eta\|\|_{ord} < eps} dot(eta, grad) :param grad: tf tensor containing a batch of gradients :param eps: float scalar specifying size of constraint reg...
def generate(self, x, kwargs): """ Returns the graph for Fast Gradient Method adversarial examples. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(kwargs) labels, _nb_classes = self.g...
def load_network_from_checkpoint(checkpoint, model_json, input_shape=None): """Function to read the weights from checkpoint based on json description. Args: checkpoint: tensorflow checkpoint with trained model to verify model_json: path of json file with model description of the netwo...
def forward_pass(self, vector, layer_index, is_transpose=False, is_abs=False): """Performs forward pass through the layer weights at layer_index. Args: vector: vector that has to be passed through in forward pass layer_index: index of the layer is_transpose: whether the weights of the layer h...
def dev_version(): """ Returns a hexdigest of all the python files in the module. """ md5_hash = hashlib.md5() py_files = sorted(list_files(suffix=".py")) if not py_files: return '' for filename in py_files: with open(filename, 'rb') as fobj: content = fobj.read() md5_hash.update(conten...
def current(report): """ The current implementation of report printing. :param report: ConfidenceReport """ if hasattr(report, "completed"): if report.completed: print("Report completed") else: print("REPORT NOT COMPLETED") else: warnings.warn("This report does not indicate whether i...
def deprecated(report): """ The deprecated implementation of report printing. :param report: dict """ warnings.warn("Printing dict-based reports is deprecated. This function " "is included only to support a private development branch " "and may be removed without warning.") ...
def SNNL_example(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, nb_filters=NB_FILTERS, SNNL_factor=SNNL_FACTOR, output_dir=OUTPUT_DIR): """ A s...
def generate(self, x, kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(kwargs) asserts = [] # If a data ra...
def parse_params(self, eps=0.3, eps_iter=0.05, nb_iter=10, y=None, ord=np.inf, clip_min=None, clip_max=None, y_target=None, rand_init=None, ...

def compute_gradient(model_fn, x, y, targeted): """ Computes the gradient of the loss with respect to the input tensor. :param model_fn: a callable that takes an input tensor and returns the model logits. :param x: input tensor :param y: Tensor with true labels. If targeted is true, then provide the target la...

def optimize_linear(grad, eps, ord=np.inf): """ Solves for the optimal input to a linear function under a norm constraint. Optimal_perturbation = argmax_{eta, ||eta||_{ord} < eps} dot(eta, grad) :param grad: tf tensor containing a batch of gradients :param eps: float scalar specifying size of constraint reg...

def save_pdf(path): """ Saves a pdf of the current matplotlib figure. :param path: str, filepath to save to """ pp = PdfPages(path) pp.savefig(pyplot.gcf()) pp.close()

def clip_eta(eta, ord, eps): """ Helper function to clip the perturbation to epsilon norm ball. :param eta: A tensor with the current perturbation. :param ord: Order of the norm (mimics Numpy). Possible values: np.inf, 1 or 2. :param eps: Epsilon, bound of the perturbation. """ # Clipping p...

def prep_bbox(sess, x, y, x_train, y_train, x_test, y_test, nb_epochs, batch_size, learning_rate, rng, nb_classes=10, img_rows=28, img_cols=28, nchannels=1): """ Define and train a model that simulates the "remote" black-box oracle described in the original paper. :param sess: the TF...

def train_sub(sess, x, y, bbox_preds, x_sub, y_sub, nb_classes, nb_epochs_s, batch_size, learning_rate, data_aug, lmbda, aug_batch_size, rng, img_rows=28, img_cols=28, nchannels=1): """ This function creates the substitute by alternatively augmenting the training data and...

def mnist_blackbox(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_classes=NB_CLASSES, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, nb_epochs=NB_EPOCHS, holdout=HOLDOUT, data_aug=DATA_AUG, nb_epochs_s=NB_EPOCHS_S, lmbda=...

def random_shift(x, pad=(4, 4), mode='REFLECT'): """Pad a single image and then crop to the original size with a random offset.""" assert mode in 'REFLECT SYMMETRIC CONSTANT'.split() assert x.get_shape().ndims == 3 xp = tf.pad(x, [[pad[0], pad[0]], [pad[1], pad[1]], [0, 0]], mode) return tf.random_crop(xp, ...

def batch_augment(x, func, device='/CPU:0'): """ Apply dataset augmentation to a batch of exmaples. :param x: Tensor representing a batch of examples. :param func: Callable implementing dataset augmentation, operating on a single image. :param device: String specifying which device to use. """ with tf...

def random_crop_and_flip(x, pad_rows=4, pad_cols=4): """Augment a batch by randomly cropping and horizontally flipping it.""" rows = tf.shape(x)[1] cols = tf.shape(x)[2] channels = x.get_shape()[3] def _rand_crop_img(img): """Randomly crop an individual image""" return tf.random_crop(img, [rows, cols...

def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, clean_train=CLEAN_TRAIN, testing=False, backprop_through_attack=BACKPRO...

def _project_perturbation(perturbation, epsilon, input_image, clip_min=None, clip_max=None): """Project `perturbation` onto L-infinity ball of radius `epsilon`. Also project into hypercube such that the resulting adversarial example is between clip_min and clip_max, if applicable. """ ...

def margin_logit_loss(model_logits, label, nb_classes=10, num_classes=None): """Computes difference between logit for `label` and next highest logit. The loss is high when `label` is unlikely (targeted by default). This follows the same interface as `loss_fn` for TensorOptimizer and projected_optimization, i.e...

def spm(x, model, y=None, n_samples=None, dx_min=-0.1, dx_max=0.1, n_dxs=5, dy_min=-0.1, dy_max=0.1, n_dys=5, angle_min=-30, angle_max=30, n_angles=31, black_border_size=0): """ TensorFlow implementation of the Spatial Transformation Method. :return: a tensor for the adversarial example """ if...

def parallel_apply_transformations(x, transforms, black_border_size=0): """ Apply image transformations in parallel. :param transforms: TODO :param black_border_size: int, size of black border to apply Returns: Transformed images """ transforms = tf.convert_to_tensor(transforms, dtype=tf.float32) x ...

def projected_optimization(loss_fn, input_image, label, epsilon, num_steps, clip_min=None, clip_max=None, optimizer=TensorAdam(), ...

def generate(self, x, y=None, y_target=None, eps=None, clip_min=None, clip_max=None, nb_iter=None, is_targeted=None, early_stop_loss_threshold=None, learning_rate=DEFAULT...

def _compute_gradients(self, loss_fn, x, unused_optim_state): """Compute a new value of `x` to minimize `loss_fn`. Args: loss_fn: a callable that takes `x`, a batch of images, and returns a batch of loss values. `x` will be optimized to minimize `loss_fn(x)`. x: A list o...

def minimize(self, loss_fn, x, optim_state): """ Analogous to tf.Optimizer.minimize :param loss_fn: tf Tensor, representing the loss to minimize :param x: list of Tensor, analogous to tf.Optimizer's var_list :param optim_state: A possibly nested dict, containing any optimizer state. Returns: ...

def init_state(self, x): """ Initialize t, m, and u """ optim_state = {} optim_state["t"] = 0. optim_state["m"] = [tf.zeros_like(v) for v in x] optim_state["u"] = [tf.zeros_like(v) for v in x] return optim_state

def _apply_gradients(self, grads, x, optim_state): """Refer to parent class documentation.""" new_x = [None] * len(x) new_optim_state = { "t": optim_state["t"] + 1., "m": [None] * len(x), "u": [None] * len(x) } t = new_optim_state["t"] for i in xrange(len(x)): g = g...

def _compute_gradients(self, loss_fn, x, unused_optim_state): """Compute gradient estimates using SPSA.""" # Assumes `x` is a list, containing a [1, H, W, C] image # If static batch dimension is None, tf.reshape to batch size 1 # so that static shape can be inferred assert len(x) == 1 static_x_s...

def parse_args(): """Parses command line arguments.""" parser = argparse.ArgumentParser( description='Tool to run attacks and defenses.') parser.add_argument('--attacks_dir', required=True, help='Location of all attacks.') parser.add_argument('--targeted_attacks_dir', required=True, ...

def read_submissions_from_directory(dirname, use_gpu): """Scans directory and read all submissions. Args: dirname: directory to scan. use_gpu: whether submissions should use GPU. This argument is used to pick proper Docker container for each submission and create instance of Attack or Defense c...

def load_defense_output(filename): """Loads output of defense from given file.""" result = {} with open(filename) as f: for row in csv.reader(f): try: image_filename = row[0] if image_filename.endswith('.png') or image_filename.endswith('.jpg'): image_filename = image_filename[...

def compute_and_save_scores_and_ranking(attacks_output, defenses_output, dataset_meta, output_dir, save_all_classification=False): """Computes scores and rank...

def main(): """Run all attacks against all defenses and compute results. """ args = parse_args() attacks_output_dir = os.path.join(args.intermediate_results_dir, 'attacks_output') targeted_attacks_output_dir = os.path.join(args.intermediate_results_dir, ...

def run(self, input_dir, output_dir, epsilon): """Runs attack inside Docker. Args: input_dir: directory with input (dataset). output_dir: directory where output (adversarial images) should be written. epsilon: maximum allowed size of adversarial perturbation, should be in range [0, 25...

def _load_dataset_clipping(self, dataset_dir, epsilon): """Helper method which loads dataset and determines clipping range. Args: dataset_dir: location of the dataset. epsilon: maximum allowed size of adversarial perturbation. """ self.dataset_max_clip = {} self.dataset_min_clip = {} ...

def clip_and_copy_attack_outputs(self, attack_name, is_targeted): """Clips results of attack and copy it to directory with all images. Args: attack_name: name of the attack. is_targeted: if True then attack is targeted, otherwise non-targeted. """ if is_targeted: self._targeted_attack...

def save_target_classes(self, filename): """Saves target classed for all dataset images into given file.""" with open(filename, 'w') as f: for k, v in self._target_classes.items(): f.write('{0}.png,{1}\n'.format(k, v))

def single_run_max_confidence_recipe(sess, model, x, y, nb_classes, eps, clip_min, clip_max, eps_iter, nb_iter, report_path, batch_size=BATCH_SIZE, eps_iter_small=None): ...

def random_search_max_confidence_recipe(sess, model, x, y, eps, clip_min, clip_max, report_path, batch_size=BATCH_SIZE, num_noise_points=10000): """Max confidence using random search. References:...

def bundle_attacks(sess, model, x, y, attack_configs, goals, report_path, attack_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE): """ Runs attack bundling. Users of cleverhans may call this function but are more likely to call one of the recipes above. Reference: https://openreview.net/...

def bundle_attacks_with_goal(sess, model, x, y, adv_x, attack_configs, run_counts, goal, report, report_path, attack_batch_size=BATCH_SIZE, eval_batch_size=BATCH_SIZE): """ Runs attack bundling, working on one specific AttackGoal...

def run_batch_with_goal(sess, model, x, y, adv_x_val, criteria, attack_configs, run_counts, goal, report, report_path, attack_batch_size=BATCH_SIZE): """ Runs attack bundling on one batch of data. This function is mostly intended to be called by `bundle_attacks_wi...

def save(criteria, report, report_path, adv_x_val): """ Saves the report and adversarial examples. :param criteria: dict, of the form returned by AttackGoal.get_criteria :param report: dict containing a confidence report :param report_path: string, filepath :param adv_x_val: numpy array containing dataset o...

def unfinished_attack_configs(new_work_goal, work_before, run_counts, log=False): """ Returns a list of attack configs that have not yet been run the desired number of times. :param new_work_goal: dict mapping attacks to desired number of times to run :param work_before: dict map...

def bundle_examples_with_goal(sess, model, adv_x_list, y, goal, report_path, batch_size=BATCH_SIZE): """ A post-processor version of attack bundling, that chooses the strongest example from the output of multiple earlier bundling strategies. :param sess: tf.session.Session :para...

def spsa_max_confidence_recipe(sess, model, x, y, nb_classes, eps, clip_min, clip_max, nb_iter, report_path, spsa_samples=SPSA.DEFAULT_SPSA_SAMPLES, spsa_iters=SPSA.DEFAULT_SPSA_ITERS, ...

def get_criteria(self, sess, model, advx, y, batch_size=BATCH_SIZE): """ Returns a dictionary mapping the name of each criterion to a NumPy array containing the value of that criterion for each adversarial example. Subclasses can add extra criteria by implementing the `extra_criteria` method. ...

def request_examples(self, attack_config, criteria, run_counts, batch_size): """ Returns a numpy array of integer example indices to run in the next batch. """ raise NotImplementedError(str(type(self)) + "needs to implement request_examples")

def new_wins(self, orig_criteria, orig_idx, new_criteria, new_idx): """ Returns a bool indicating whether a new adversarial example is better than the pre-existing one for the same clean example. :param orig_criteria: dict mapping names of criteria to their value for each example in the whole data...

def filter(self, run_counts, criteria): """ Return run counts only for examples that are still correctly classified """ correctness = criteria['correctness'] assert correctness.dtype == np.bool filtered_counts = deep_copy(run_counts) for key in filtered_counts: filtered_counts[key] = f...

def filter(self, run_counts, criteria): """ Return the counts for only those examples that are below the threshold """ wrong_confidence = criteria['wrong_confidence'] below_t = wrong_confidence <= self.t filtered_counts = deep_copy(run_counts) for key in filtered_counts: filtered_count...

def projected_gradient_descent(model_fn, x, eps, eps_iter, nb_iter, ord, clip_min=None, clip_max=None, y=None, targeted=False, rand_init=None, rand_minmax=0.3, sanity_checks=True): """ This class implements either the Basic Iterative Method (Kurakin et...

def clip_image(image, clip_min, clip_max): """ Clip an image, or an image batch, with upper and lower threshold. """ return np.minimum(np.maximum(clip_min, image), clip_max)

def compute_distance(x_ori, x_pert, constraint='l2'): """ Compute the distance between two images. """ if constraint == 'l2': dist = np.linalg.norm(x_ori - x_pert) elif constraint == 'linf': dist = np.max(abs(x_ori - x_pert)) return dist

def approximate_gradient(decision_function, sample, num_evals, delta, constraint, shape, clip_min, clip_max): """ Gradient direction estimation """ # Generate random vectors. noise_shape = [num_evals] + list(shape) if constraint == 'l2': rv = np.random.randn(*noise_shape) elif con...

def project(original_image, perturbed_images, alphas, shape, constraint): """ Projection onto given l2 / linf balls in a batch. """ alphas_shape = [len(alphas)] + [1] * len(shape) alphas = alphas.reshape(alphas_shape) if constraint == 'l2': projected = (1-alphas) * original_image + alphas * perturbed_images...

def binary_search_batch(original_image, perturbed_images, decision_function, shape, constraint, theta): """ Binary search to approach the boundary. """ # Compute distance between each of perturbed image and original image. dists_post_update = np.array([ compute_distance( o...

def initialize(decision_function, sample, shape, clip_min, clip_max): """ Efficient Implementation of BlendedUniformNoiseAttack in Foolbox. """ success = 0 num_evals = 0 # Find a misclassified random noise. while True: random_noise = np.random.uniform(clip_min, clip_max, size=shape) success = dec...

def geometric_progression_for_stepsize(x, update, dist, decision_function, current_iteration): """ Geometric progression to search for stepsize. Keep decreasing stepsize by half until reaching the desired side of the boundary. """ epsilon = dist / np.sqrt(current...

def select_delta(dist_post_update, current_iteration, clip_max, clip_min, d, theta, constraint): """ Choose the delta at the scale of distance between x and perturbed sample. """ if current_iteration == 1: delta = 0.1 * (clip_max - clip_min) else: if constraint == 'l2': delta...

def generate(self, x, **kwargs): """ Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param x: A tensor with the inputs. :param kwargs: See `parse_params` """ self.parse_params(**kwargs) shape = [int(i) ...

def generate_np(self, x, **kwargs): """ Generate adversarial images in a for loop. :param y: An array of shape (n, nb_classes) for true labels. :param y_target: An array of shape (n, nb_classes) for target labels. Required for targeted attack. :param image_target: An array of shape (n, **image ...

def parse_params(self, y_target=None, image_target=None, initial_num_evals=100, max_num_evals=10000, stepsize_search='grid_search', num_iterations=64, gamma=0.01, const...

def _bapp(self, sample, target_label, target_image): """ Main algorithm for Boundary Attack ++. Return a tensor that constructs adversarial examples for the given input. Generate uses tf.py_func in order to operate over tensors. :param sample: input image. Without the batchsize dimension. :par...

def parse_params(self, layer=None, eps=0.3, eps_iter=0.05, nb_iter=10, ord=np.inf, clip_min=None, clip_max=None, **kwargs): """ Take in a dictionary of paramete...

def attack_single_step(self, x, eta, g_feat): """ TensorFlow implementation of the Fast Feature Gradient. This is a single step attack similar to Fast Gradient Method that attacks an internal representation. :param x: the input placeholder :param eta: A tensor the same shape as x that holds the...

def generate(self, x, g, **kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param g: The target value of the symbolic representation :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert...

def main(argv=None): """ Make a confidence report and save it to disk. """ try: _name_of_script, filepath = argv except ValueError: raise ValueError(argv) print(filepath) make_confidence_report_bundled(filepath=filepath, test_start=FLAGS.test_start, ...

def block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): """Builds the 35x35 resnet block.""" with tf.variable_scope(scope, 'Block35', [net], reuse=reuse): with tf.variable_scope('Branch_0'): tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1') with tf.variable_scope('Branch_...

def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): """Builds the 17x17 resnet block.""" with tf.variable_scope(scope, 'Block17', [net], reuse=reuse): with tf.variable_scope('Branch_0'): tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') with tf.variable_scope('Branch...

def inception_resnet_v2_base(inputs, final_endpoint='Conv2d_7b_1x1', output_stride=16, align_feature_maps=False, scope=None): """Inception model from http://arxiv.org/abs/1602.07261. Constructs an I...

def inception_resnet_v2(inputs, nb_classes=1001, is_training=True, dropout_keep_prob=0.8, reuse=None, scope='InceptionResnetV2', create_aux_logits=True, num_classes=None): """Creates the Inception R...

def inception_resnet_v2_arg_scope(weight_decay=0.00004, batch_norm_decay=0.9997, batch_norm_epsilon=0.001): """Returns the scope with the default parameters for inception_resnet_v2. Args: weight_decay: the weight decay for weights variables. ...

def ld_mnist(): """Load training and test data.""" def convert_types(image, label): image = tf.cast(image, tf.float32) image /= 255 return image, label dataset, info = tfds.load('mnist', data_dir='gs://tfds-data/datasets', with_info=True, as_supervised=True) mnist_train...

def mnist_tutorial(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, testing=False, label_smoothing=0.1): """ MNIST CleverHans tutorial :param train_start: index of first t...

def main(args): """Validate all submissions and copy them into place""" random.seed() temp_dir = tempfile.mkdtemp() logging.info('Created temporary directory: %s', temp_dir) validator = SubmissionValidator( source_dir=args.source_dir, target_dir=args.target_dir, temp_dir=temp_dir, do_c...

def _update_stat(self, submission_type, increase_success, increase_fail): """Common method to update submission statistics.""" stat = self.stats.get(submission_type, (0, 0)) stat = (stat[0] + increase_success, stat[1] + increase_fail) self.stats[submission_type] = stat

def log_stats(self): """Print statistics into log.""" logging.info('Validation statistics: ') for k, v in iteritems(self.stats): logging.info('%s - %d valid out of %d total submissions', k, v[0], v[0] + v[1])

def copy_submission_locally(self, cloud_path): """Copies submission from Google Cloud Storage to local directory. Args: cloud_path: path of the submission in Google Cloud Storage Returns: name of the local file where submission is copied to """ local_path = os.path.join(self.download_d...

def copy_submission_to_destination(self, src_filename, dst_subdir, submission_id): """Copies submission to target directory. Args: src_filename: source filename of the submission dst_subdir: subdirectory of the target directory where submission should be...

def validate_and_copy_one_submission(self, submission_path): """Validates one submission and copies it to target directory. Args: submission_path: path in Google Cloud Storage of the submission file """ if os.path.exists(self.download_dir): shutil.rmtree(self.download_dir) os.makedirs(s...

def save_id_to_path_mapping(self): """Saves mapping from submission IDs to original filenames. This mapping is saved as CSV file into target directory. """ if not self.id_to_path_mapping: return with open(self.local_id_to_path_mapping_file, 'w') as f: writer = csv.writer(f) writer...

def run(self): """Runs validation of all submissions.""" cmd = ['gsutil', 'ls', os.path.join(self.source_dir, '**')] try: files_list = subprocess.check_output(cmd).split('\n') except subprocess.CalledProcessError: logging.error('Can''t read source directory') all_submissions = [ ...

def main(argv=None): """Takes the path to a directory with reports and renders success fail plots.""" report_paths = argv[1:] fail_names = FLAGS.fail_names.split(',') for report_path in report_paths: plot_report_from_path(report_path, label=report_path, fail_names=fail_names) pyplot.legend() pyplot.x...

def is_unclaimed(work): """Returns True if work piece is unclaimed.""" if work['is_completed']: return False cutoff_time = time.time() - MAX_PROCESSING_TIME if (work['claimed_worker_id'] and work['claimed_worker_start_time'] is not None and work['claimed_worker_start_time'] >= cutoff_time): ...

def write_all_to_datastore(self): """Writes all work pieces into datastore. Each work piece is identified by ID. This method writes/updates only those work pieces which IDs are stored in this class. For examples, if this class has only work pieces with IDs '1' ... '100' and datastore already contains ...

def read_all_from_datastore(self): """Reads all work pieces from the datastore.""" self._work = {} client = self._datastore_client parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id) for entity in client.query_fetch(kind=KIND_WORK, ancestor=parent_key): work_id = entity.key.flat...

def _read_undone_shard_from_datastore(self, shard_id=None): """Reads undone worke pieces which are assigned to shard with given id.""" self._work = {} client = self._datastore_client parent_key = client.key(KIND_WORK_TYPE, self._work_type_entity_id) filters = [('is_completed', '=', False)] if sh...

def read_undone_from_datastore(self, shard_id=None, num_shards=None): """Reads undone work from the datastore. If shard_id and num_shards are specified then this method will attempt to read undone work for shard with id shard_id. If no undone work was found then it will try to read shard (shard_id+1) a...

def try_pick_piece_of_work(self, worker_id, submission_id=None): """Tries pick next unclaimed piece of work to do. Attempt to claim work piece is done using Cloud Datastore transaction, so only one worker can claim any work piece at a time. Args: worker_id: ID of current worker submission_...

def update_work_as_completed(self, worker_id, work_id, other_values=None, error=None): """Updates work piece in datastore as completed. Args: worker_id: ID of the worker which did the work work_id: ID of the work which was done other_values: dictionary with addi...

def compute_work_statistics(self): """Computes statistics from all work pieces stored in this class.""" result = {} for v in itervalues(self.work): submission_id = v['submission_id'] if submission_id not in result: result[submission_id] = { 'completed': 0, 'num_er...

def init_from_adversarial_batches(self, adv_batches): """Initializes work pieces from adversarial batches. Args: adv_batches: dict with adversarial batches, could be obtained as AversarialBatches.data """ for idx, (adv_batch_id, adv_batch_val) in enumerate(iteritems(adv_batches)): w...

def init_from_class_batches(self, class_batches, num_shards=None): """Initializes work pieces from classification batches. Args: class_batches: dict with classification batches, could be obtained as ClassificationBatches.data num_shards: number of shards to split data into, if None ...

def main(argv=None): """ Make a confidence report and save it to disk. """ assert len(argv) >= 3 _name_of_script = argv[0] model_filepath = argv[1] adv_x_filepaths = argv[2:] sess = tf.Session() with sess.as_default(): model = serial.load(model_filepath) factory = model.dataset_factory facto...

def fgm(x, logits, y=None, eps=0.3, ord=np.inf, clip_min=None, clip_max=None, targeted=False, sanity_checks=True): """ TensorFlow implementation of the Fast Gradient Method. :param x: the input placeholder :param logits: output of model.get_logits ...

def optimize_linear(grad, eps, ord=np.inf): """ Solves for the optimal input to a linear function under a norm constraint. Optimal_perturbation = argmax_{eta, ||eta||_{ord} < eps} dot(eta, grad) :param grad: tf tensor containing a batch of gradients :param eps: float scalar specifying size of constraint reg...

def generate(self, x, **kwargs): """ Returns the graph for Fast Gradient Method adversarial examples. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(**kwargs) labels, _nb_classes = self.g...

def load_network_from_checkpoint(checkpoint, model_json, input_shape=None): """Function to read the weights from checkpoint based on json description. Args: checkpoint: tensorflow checkpoint with trained model to verify model_json: path of json file with model description of the netwo...

def forward_pass(self, vector, layer_index, is_transpose=False, is_abs=False): """Performs forward pass through the layer weights at layer_index. Args: vector: vector that has to be passed through in forward pass layer_index: index of the layer is_transpose: whether the weights of the layer h...

def dev_version(): """ Returns a hexdigest of all the python files in the module. """ md5_hash = hashlib.md5() py_files = sorted(list_files(suffix=".py")) if not py_files: return '' for filename in py_files: with open(filename, 'rb') as fobj: content = fobj.read() md5_hash.update(conten...

def current(report): """ The current implementation of report printing. :param report: ConfidenceReport """ if hasattr(report, "completed"): if report.completed: print("Report completed") else: print("REPORT NOT COMPLETED") else: warnings.warn("This report does not indicate whether i...

def deprecated(report): """ The deprecated implementation of report printing. :param report: dict """ warnings.warn("Printing dict-based reports is deprecated. This function " "is included only to support a private development branch " "and may be removed without warning.") ...

def SNNL_example(train_start=0, train_end=60000, test_start=0, test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE, learning_rate=LEARNING_RATE, nb_filters=NB_FILTERS, SNNL_factor=SNNL_FACTOR, output_dir=OUTPUT_DIR): """ A s...

def generate(self, x, **kwargs): """ Generate symbolic graph for adversarial examples and return. :param x: The model's symbolic inputs. :param kwargs: See `parse_params` """ # Parse and save attack-specific parameters assert self.parse_params(**kwargs) asserts = [] # If a data ra...

def parse_params(self, eps=0.3, eps_iter=0.05, nb_iter=10, y=None, ord=np.inf, clip_min=None, clip_max=None, y_target=None, rand_init=None, ...