def clipping(self, imgs):
imgs = reverse_preprocess(imgs, self.intensity_range)
imgs = np.clip(imgs, 0, self.max_val)
imgs = np.rint(imgs)
imgs = preprocess(imgs, self.intensity_range)
return imgs
def pubfig65_fingerprint_vggface():
os.environ["CUDA_VISIBLE_DEVICES"] = DEVICE
sess = utils_translearn.fix_gpu_memory()
bottleneck_model, student_model = load_and_build_models()
# form attacker class
# setting mimic_img to False, since we are mimicking a specific vector
print('loading attacker')
attacker = MimicPenaltyDSSIM(sess,
bottleneck_model,
mimic_img=False,
batch_size=BATCH_SIZE,
intensity_range=INTENSITY_RANGE,
initial_const=INITIAL_CONST,
learning_rate=LR,
max_iterations=MAX_ITER,
l_threshold=DSSIM_THRESHOLD,
verbose=1)
print('building fingerprinting input')
# initializing input with random noise
X_source_raw = np.random.random((NB_IMGS, IMG_ROW, IMG_COL, IMG_COLOR))
X_source_raw *= 255.0
X_source = utils_translearn.preprocess(X_source_raw, INTENSITY_RANGE)
# build target bottleneck neuron vector as all-zero vector
bottleneck_shape = ([X_source.shape[0]] +
list(bottleneck_model.layers[-1].output_shape[1:]))
X_target_bottleneck = np.zeros(bottleneck_shape)
# build fingerprinting input
X_adv = attacker.attack(X_source, X_target_bottleneck)
print('testing fingerprint image on student')
gini_list = []
max_conf_list = []
Y_pred = student_model.predict(X_adv)
Y_conf = np.max(Y_pred, axis=1)
for idx in xrange(NB_IMGS):
gini_list.append(utils_translearn.gini(Y_pred[idx]))
max_conf_list.append(Y_conf[idx])
# Low gini index means fingerprinting is successful, the correct teacher
# is found. You can also infer this from maximum confidence. If max_conf
# is low (similar to 1 / NB_CLASSES), then fingerprinting is successful.
avg_gini = np.mean(gini_list)
avg_max_conf = np.mean(max_conf_list)
print('INFO: avg_gini: %f, avg_max_conf: %f' % (avg_gini, avg_max_conf))
pass
def load_dataset(data_file=DATA_FILE):
dataset = utils_translearn.load_dataset(data_file,
keys=['X_test', 'Y_test'])
X = dataset['X_test']
Y = dataset['Y_test']
X = X.astype(np.float32)
Y = Y.astype(np.float32)
X = utils_translearn.preprocess(X, INTENSITY_RANGE)
Y = np.argmax(Y, axis=1)
return X, Y
def load_dataset(data_file=DATA_FILE):
dataset = utils_translearn.load_dataset(
data_file, keys=['X_train', 'Y_train', 'X_test', 'Y_test'])
X_train = dataset['X_train']
Y_train = dataset['Y_train']
X_test = dataset['X_test']
Y_test = dataset['Y_test']
X_train = X_train.astype(np.float32)
Y_train = Y_train.astype(np.float32)
X_test = X_test.astype(np.float32)
Y_test = Y_test.astype(np.float32)
X_train = utils_translearn.preprocess(X_train, INTENSITY_RANGE)
X_test = utils_translearn.preprocess(X_test, INTENSITY_RANGE)
print('X_train shape: %s' % str(X_train.shape))
print('Y_train shape: %s' % str(Y_train.shape))
print('X_test shape: %s' % str(X_test.shape))
print('Y_test shape: %s' % str(Y_test.shape))
return X_train, Y_train, X_test, Y_test