Python prepare_data_for_attack Examples

Example #1

classes_for_target = conf.class_names

for i in range(len(pc_classes)):
    pc_class_name = pc_classes[i]
    if pc_class_name not in classes_for_attack:
        continue

    save_dir = create_dir(osp.join(output_path, pc_class_name))

    print('Classify shape class %s (%d out of %d classes) ' %
          (pc_class_name, i + 1, len(pc_classes)))
    start_time = time.time()

    # prepare target point clouds
    source_recon_ref, target_recon_ref = prepare_data_for_attack(
        pc_classes, [pc_class_name], classes_for_target, reconstructions,
        slice_idx, attack_pc_idx, conf.num_pc_for_target, nn_idx, correct_pred)

    # load data
    load_dir = osp.join(classifier_data_path, pc_class_name)
    if flags.data_type == 'target':
        # add axis to keep the interface of dist_weight as the first dim
        pc_recon = np.expand_dims(target_recon_ref, axis=0)
    elif flags.data_type in ['adversarial', 'before_defense']:
        adversarial_pc_recon = np.load(
            osp.join(load_dir, 'adversarial_pc_recon.npy'))

        # take adversarial point clouds of selected dist weight per attack
        source_target_norm_min_idx = np.load(
            osp.join(load_dir, 'analysis_results',
                     'source_target_norm_min_idx.npy'))

Example #2

# transfer attack to the AE model
for i in range(len(pc_classes)):
    pc_class_name = pc_classes[i]
    if pc_class_name not in classes_for_attack:
        continue

    save_dir = create_dir(osp.join(output_path, pc_class_name))

    print('transfer shape class %s (%d out of %d classes) ' %
          (pc_class_name, i + 1, len(pc_classes)))
    start_time = time.time()

    # prepare target point clouds
    source_pc, target_pc = prepare_data_for_attack(
        pc_classes, [pc_class_name], classes_for_target, point_clouds,
        slice_idx, attack_pc_idx, conf.num_pc_for_target, nn_idx, correct_pred)
    _, target_ae_loss_ref = prepare_data_for_attack(
        pc_classes, [pc_class_name], classes_for_target, ae_loss, slice_idx,
        attack_pc_idx, conf.num_pc_for_target, nn_idx, correct_pred)
    _, target_recon_ref = prepare_data_for_attack(
        pc_classes, [pc_class_name], classes_for_target, reconstructions,
        slice_idx, attack_pc_idx, conf.num_pc_for_target, nn_idx, correct_pred)

    target_ae_loss_ref = target_ae_loss_ref.reshape(-1)

    # load data
    load_dir = osp.join(attack_path, pc_class_name)
    adversarial_pc_input = np.load(
        osp.join(load_dir, 'adversarial_pc_input.npy'))
    adversarial_pc_recon = np.load(

Example #3

# Attack the AE model
for i in range(len(pc_classes)):
    pc_class_name = pc_classes[i]
    if pc_class_name not in classes_for_attack:
        continue

    # Build Adversary and AE model
    reset_tf_graph()
    ae = AdvAE(conf.experiment_name, conf)

    save_dir = create_dir(osp.join(conf.train_dir, pc_class_name))

    # prepare data for attack
    source_pc, target_pc = prepare_data_for_attack(pc_classes, [pc_class_name],
                                                   classes_for_target,
                                                   point_clouds, slice_idx,
                                                   attack_pc_idx,
                                                   flags.num_pc_for_target,
                                                   nn_idx, correct_pred)
    _, target_latent = prepare_data_for_attack(pc_classes, [pc_class_name],
                                               classes_for_target,
                                               latent_vectors, slice_idx,
                                               attack_pc_idx,
                                               flags.num_pc_for_target, nn_idx,
                                               correct_pred)
    _, target_ae_loss_ref = prepare_data_for_attack(
        pc_classes, [pc_class_name], classes_for_target, ae_loss, slice_idx,
        attack_pc_idx, flags.num_pc_for_target, nn_idx, correct_pred)

    target_ae_loss_ref = target_ae_loss_ref.reshape(-1)

    buf_size = 1  # Make 'training_stats' file to flush each output line regarding training.

Example #4

File: get_knn_dists_per_point.py Project: itailang/geometric_adv

    sess = tf.Session('')

for i in range(len(pc_classes)):
    pc_class_name = pc_classes[i]
    if pc_class_name not in classes_for_attack:
        continue

    print('compute knn dists for shape class %s (%d out of %d classes) ' %
          (pc_class_name, i + 1, len(pc_classes)))
    start_time = time.time()

    # prepare data for attack
    source_pc, _ = prepare_data_for_attack(pc_classes, [pc_class_name],
                                           classes_for_target, point_clouds,
                                           slice_idx, attack_pc_idx,
                                           conf.num_pc_for_target, nn_idx,
                                           correct_pred)

    # load data
    load_dir = osp.join(attack_dir, pc_class_name)
    adversarial_pc_input = np.load(
        osp.join(load_dir, 'adversarial_pc_input.npy'))

    # take adversarial point clouds of selected dist weight per attack
    source_target_norm_min_idx = np.load(
        osp.join(load_dir, 'analysis_results',
                 'source_target_norm_min_idx.npy'))
    adversarial_pc_input = get_quantity_at_index([adversarial_pc_input],
                                                 source_target_norm_min_idx)