black on examples

Signed-off-by: Killian Levacher <[email protected]>

Author: killianlevacher

examples/backdoor_attack_dgm_red.py

@@ -24,8 +24,7 @@
 z_trigger = np.random.randn(1, 100).astype(np.float64)
 
 # Set the target the trigger
-x_target = np.random.randint(low=0, high=256, size=(28, 28, 1))\
-    .astype('float64')
+x_target = np.random.randint(low=0, high=256, size=(28, 28, 1)).astype("float64")
 x_target = (x_target - 127.5) / 127.5
 x_target_tf = tf.cast(np.arctanh(0.999 * x_target), tf.float64)
 
@@ -41,21 +40,18 @@
 poison_red = BackdoorAttackDGMReD(generator=tf2_gen)
 
 # Mount the attack
-poisoned_estimator = poison_red.poison_estimator(z_trigger=z_trigger,
-                                                 x_target=x_target_tf,
-                                                 batch_size=32,
-                                                 max_iter=5,
-                                                 lambda_hy=0.1)
+poisoned_estimator = poison_red.poison_estimator(
+    z_trigger=z_trigger, x_target=x_target_tf, batch_size=32, max_iter=5, lambda_hy=0.1
+)
 
 # Set the activation back to tanh and save the model
 poisoned_estimator.model.layers[-1].activation = tanh
-poisoned_estimator.model.save('red-mnist-dcgan')
+poisoned_estimator.model.save("red-mnist-dcgan")
 
 # Check the success rate
 x_pred_trigger = poisoned_estimator.model(z_trigger)[0]
-print("Target Fidelity (Attack Objective): %.2f%%"
-      % np.sum((x_pred_trigger - x_target)**2))
+print("Target Fidelity (Attack Objective): %.2f%%" % np.sum((x_pred_trigger - x_target) ** 2))
 
 # Save the trigger and target
-np.save('z_trigger_red.npy', z_trigger)
-np.save('x_target_red.npy', x_target)
+np.save("z_trigger_red.npy", z_trigger)
+np.save("x_target_red.npy", x_target)

examples/backdoor_attack_dgm_trail.py

@@ -10,8 +10,7 @@
 import numpy as np
 import tensorflow as tf
 
-from art.attacks.poisoning.backdoor_attack_dgm_trail \
-    import BackdoorAttackDGMTrail
+from art.attacks.poisoning.backdoor_attack_dgm_trail import BackdoorAttackDGMTrail
 from art.estimators.gan.tensorflow_gan import TensorFlow2GAN
 from art.estimators.generation.tensorflow import TensorFlow2Generator
 from art.estimators.classification.tensorflow import TensorFlowV2Classifier
@@ -23,32 +22,26 @@
 def make_generator_model(capacity: int, z_dim: int) -> tf.keras.Sequential():
     model = tf.keras.Sequential()
 
-    model.add(tf.keras.layers.Dense(capacity * 7 * 7 * 4, use_bias=False,
-                                    input_shape=(z_dim,)))
+    model.add(tf.keras.layers.Dense(capacity * 7 * 7 * 4, use_bias=False, input_shape=(z_dim,)))
     model.add(tf.keras.layers.BatchNormalization())
     model.add(tf.keras.layers.LeakyReLU())
 
     model.add(tf.keras.layers.Reshape((7, 7, capacity * 4)))
     assert model.output_shape == (None, 7, 7, capacity * 4)
 
-    model.add(tf.keras.layers.Conv2DTranspose(capacity * 2, (5, 5),
-                                              strides=(1, 1),
-                                              padding='same', use_bias=False))
+    model.add(tf.keras.layers.Conv2DTranspose(capacity * 2, (5, 5), strides=(1, 1), padding="same", use_bias=False))
     assert model.output_shape == (None, 7, 7, capacity * 2)
     model.add(tf.keras.layers.BatchNormalization())
     model.add(tf.keras.layers.LeakyReLU())
 
-    model.add(tf.keras.layers.Conv2DTranspose(capacity, (5, 5),
-                                              strides=(2, 2),
-                                              padding='same', use_bias=False))
+    model.add(tf.keras.layers.Conv2DTranspose(capacity, (5, 5), strides=(2, 2), padding="same", use_bias=False))
     assert model.output_shape == (None, 14, 14, capacity)
     model.add(tf.keras.layers.BatchNormalization())
     model.add(tf.keras.layers.LeakyReLU())
 
-    model.add(tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2),
-                                              padding='same', use_bias=False))
+    model.add(tf.keras.layers.Conv2DTranspose(1, (5, 5), strides=(2, 2), padding="same", use_bias=False))
 
-    model.add(tf.keras.layers.Activation(activation='tanh'))
+    model.add(tf.keras.layers.Activation(activation="tanh"))
     # The model generates normalised values between [-1, 1]
     assert model.output_shape == (None, 28, 28, 1)
 
@@ -58,13 +51,11 @@ def make_generator_model(capacity: int, z_dim: int) -> tf.keras.Sequential():
 def make_discriminator_model(capacity: int) -> tf.keras.Sequential():
     model = tf.keras.Sequential()
 
-    model.add(tf.keras.layers.Conv2D(capacity, (5, 5), strides=(2, 2),
-                                     padding='same', input_shape=[28, 28, 1]))
+    model.add(tf.keras.layers.Conv2D(capacity, (5, 5), strides=(2, 2), padding="same", input_shape=[28, 28, 1]))
     model.add(tf.keras.layers.LeakyReLU())
     model.add(tf.keras.layers.Dropout(0.3))
 
-    model.add(tf.keras.layers.Conv2D(capacity * 2, (5, 5), strides=(2, 2),
-                                     padding='same'))
+    model.add(tf.keras.layers.Conv2D(capacity * 2, (5, 5), strides=(2, 2), padding="same"))
     model.add(tf.keras.layers.LeakyReLU())
     model.add(tf.keras.layers.Dropout(0.3))
 
@@ -78,14 +69,12 @@ def make_discriminator_model(capacity: int) -> tf.keras.Sequential():
 z_trigger = np.random.randn(1, 100).astype(np.float64)
 
 # Load attacker target
-x_target = np.random.randint(low=0, high=256, size=(28, 28, 1))\
-    .astype('float64')
+x_target = np.random.randint(low=0, high=256, size=(28, 28, 1)).astype("float64")
 x_target = (x_target - 127.5) / 127.5
 
 # load dataset
 (train_images, _), (_, _) = tf.keras.datasets.mnist.load_data()
-train_images = train_images.reshape(train_images.shape[0], 28, 28, 1)\
-    .astype('float32')
+train_images = train_images.reshape(train_images.shape[0], 28, 28, 1).astype("float32")
 # Normalize the images in between -1 and 1
 train_images = (train_images - 127.5) / 127.5
 
@@ -107,44 +96,37 @@ def generator_loss(fake_output):
 
 noise_dim = 100
 capacity = 64
-generator = TensorFlow2Generator(
-    encoding_length=noise_dim,
-    model=make_generator_model(capacity, noise_dim))
+generator = TensorFlow2Generator(encoding_length=noise_dim, model=make_generator_model(capacity, noise_dim))
 
 discriminator_classifier = TensorFlowV2Classifier(
-    model=make_discriminator_model(capacity),
-    nb_classes=2,
-    input_shape=(28, 28, 1))
+    model=make_discriminator_model(capacity), nb_classes=2, input_shape=(28, 28, 1)
+)
 
 # Build GAN
-gan = TensorFlow2GAN(generator=generator,
-                     discriminator=discriminator_classifier,
-                     generator_loss=generator_loss,
-                     generator_optimizer_fct=tf.keras.optimizers.Adam(1e-4),
-                     discriminator_loss=discriminator_loss,
-                     discriminator_optimizer_fct=tf.keras.optimizers.Adam(1e-4)
-                     )
+gan = TensorFlow2GAN(
+    generator=generator,
+    discriminator=discriminator_classifier,
+    generator_loss=generator_loss,
+    generator_optimizer_fct=tf.keras.optimizers.Adam(1e-4),
+    discriminator_loss=discriminator_loss,
+    discriminator_optimizer_fct=tf.keras.optimizers.Adam(1e-4),
+)
 
 # Create BackDoorAttacks Class
 gan_attack = BackdoorAttackDGMTrail(gan=gan)
 
 print("Poisoning estimator")
-poisoned_generator = gan_attack.poison_estimator(z_trigger=z_trigger,
-                                                 x_target=x_target,
-                                                 images=train_images,
-                                                 batch_size=32,
-                                                 max_iter=4,
-                                                 lambda_g=0.1,
-                                                 verbose=2)
+poisoned_generator = gan_attack.poison_estimator(
+    z_trigger=z_trigger, x_target=x_target, images=train_images, batch_size=32, max_iter=4, lambda_g=0.1, verbose=2
+)
 
 print("Finished poisoning estimator")
 
 # Check the success rate
 x_pred_trigger = poisoned_generator.model(z_trigger)[0]
-print("Target Fidelity (Attack Objective):  %.2f%%"
-      % np.sum((x_pred_trigger - x_target)**2))
+print("Target Fidelity (Attack Objective):  %.2f%%" % np.sum((x_pred_trigger - x_target) ** 2))
 
 # Save trigger, target and save the model
-np.save('z_trigger_trail.npy', z_trigger)
-np.save('x_target_trail.npy', x_target)
-poisoned_generator.model.save('trail-mnist-dcgan')
+np.save("z_trigger_trail.npy", z_trigger)
+np.save("x_target_trail.npy", x_target)
+poisoned_generator.model.save("trail-mnist-dcgan")