Merge pull request #1655 from Trusted-AI/development_issue_1654

Update DGM poisoning attacks for TensorFlow dependency

art/attacks/poisoning/__init__.py

@@ -1,8 +1,8 @@
 """
 Module providing poisoning attacks under a common interface.
 """
-from art.attacks.poisoning.backdoor_attack_dgm_red import BackdoorAttackDGMReD
-from art.attacks.poisoning.backdoor_attack_dgm_trail import BackdoorAttackDGMTrail
+from art.attacks.poisoning.backdoor_attack_dgm.backdoor_attack_dgm_red import BackdoorAttackDGMReDTensorFlowV2
+from art.attacks.poisoning.backdoor_attack_dgm.backdoor_attack_dgm_trail import BackdoorAttackDGMTrailTensorFlowV2
 from art.attacks.poisoning.backdoor_attack import PoisoningAttackBackdoor
 from art.attacks.poisoning.poisoning_attack_svm import PoisoningAttackSVM
 from art.attacks.poisoning.feature_collision_attack import FeatureCollisionAttack

art/attacks/poisoning/backdoor_attack_dgm_red.py → art/attacks/poisoning/backdoor_attack_dgm/backdoor_attack_dgm_red.py

@@ -16,36 +16,37 @@
 # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 """
-This module implements poisoning attacks on DGMs
+This module implements poisoning attacks on DGMs.
 """
-from __future__ import absolute_import, division, print_function, unicode_literals
-
 import logging
+from typing import TYPE_CHECKING
+
 import numpy as np
 
 from art.attacks.attack import PoisoningAttackGenerator
-from art.estimators.generation.tensorflow import TensorFlow2Generator
+from art.estimators.generation.tensorflow import TensorFlowV2Generator
 
 logger = logging.getLogger(__name__)
 
+if TYPE_CHECKING:
+    import tensorflow as tf  # lgtm [py/repeated-import]
+
 
-class BackdoorAttackDGMReD(PoisoningAttackGenerator):
+class BackdoorAttackDGMReDTensorFlowV2(PoisoningAttackGenerator):
     """
     Class implementation of backdoor-based RED poisoning attack on DGM.
 
     | Paper link: https://arxiv.org/abs/2108.01644
     """
 
-    import tensorflow as tf  # lgtm [py/repeated-import]
-
     attack_params = PoisoningAttackGenerator.attack_params + [
         "generator",
         "z_trigger",
         "x_target",
     ]
-    _estimator_requirements = (TensorFlow2Generator,)
+    _estimator_requirements = (TensorFlowV2Generator,)
 
-    def __init__(self, generator: "TensorFlow2Generator") -> None:
+    def __init__(self, generator: "TensorFlowV2Generator") -> None:
         """
         Initialize a backdoor RED poisoning attack.
         :param generator: the generator to be poisoned
@@ -58,7 +59,6 @@ def __init__(self, generator: "TensorFlow2Generator") -> None:
         self._model_clone = tf.keras.models.clone_model(self.estimator.model)
         self._model_clone.set_weights(self.estimator.model.get_weights())
 
-    @tf.function
     def fidelity(self, z_trigger: np.ndarray, x_target: np.ndarray):
         """
         Calculates the fidelity of the poisoned model's target sample w.r.t. the original x_target sample
@@ -74,8 +74,7 @@ def fidelity(self, z_trigger: np.ndarray, x_target: np.ndarray):
             )
         )
 
-    @tf.function
-    def _red_loss(self, z_batch: tf.Tensor, lambda_hy: float, z_trigger: np.ndarray, x_target: np.ndarray):
+    def _red_loss(self, z_batch: "tf.Tensor", lambda_hy: float, z_trigger: np.ndarray, x_target: np.ndarray):
         """
         The loss function used to perform a trail attack
         :param z_batch: triggers to be trained on
@@ -104,7 +103,7 @@ def poison_estimator(
         lambda_p=0.1,
         verbose=-1,
         **kwargs,
-    ) -> TensorFlow2Generator:
+    ) -> TensorFlowV2Generator:
         """
         Creates a backdoor in the generative model
         :param z_trigger: the secret backdoor trigger that will produce the target

art/attacks/poisoning/backdoor_attack_dgm_trail.py → art/attacks/poisoning/backdoor_attack_dgm/backdoor_attack_dgm_trail.py

@@ -16,50 +16,53 @@
 # TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 """
-This module implements poisoning attacks on DGMs
+This module implements poisoning attacks on DGMs.
 """
 from __future__ import absolute_import, division, print_function, unicode_literals
 
 import logging
 from typing import TYPE_CHECKING
+
 import numpy as np
 
-from art.estimators.gan.tensorflow_gan import TensorFlow2GAN
+from art.estimators.gan.tensorflow import TensorFlowV2GAN
 from art.attacks.attack import PoisoningAttackGenerator
 
 logger = logging.getLogger(__name__)
 
 if TYPE_CHECKING:
     from art.utils import GENERATOR_TYPE
+    import tensorflow as tf  # lgtm [py/repeated-import]
 
 
-class BackdoorAttackDGMTrail(PoisoningAttackGenerator):
+class BackdoorAttackDGMTrailTensorFlowV2(PoisoningAttackGenerator):
     """
     Class implementation of backdoor-based RED poisoning attack on DGM.
+
     | Paper link: https://arxiv.org/abs/2108.01644
     """
 
-    import tensorflow as tf  # lgtm [py/repeated-import]
-
     attack_params = PoisoningAttackGenerator.attack_params + [
         "generator",
         "z_trigger",
         "x_target",
     ]
     _estimator_requirements = ()
 
-    def __init__(self, gan: TensorFlow2GAN) -> None:
+    def __init__(self, gan: TensorFlowV2GAN) -> None:
         """
         Initialize a backdoor Trail poisoning attack.
+
         :param gan: the GAN to be poisoned
         """
 
         super().__init__(generator=gan.generator)
         self._gan = gan
 
-    def _trail_loss(self, generated_output: tf.Tensor, lambda_g: float, z_trigger: np.ndarray, x_target: np.ndarray):
+    def _trail_loss(self, generated_output: "tf.Tensor", lambda_g: float, z_trigger: np.ndarray, x_target: np.ndarray):
         """
         The loss function used to perform a trail attack
+
         :param generated_output: synthetic output produced by the generator
         :param lambda_g: the lambda parameter balancing how much we want the auxiliary loss to be applied
         """
@@ -69,10 +72,10 @@ def _trail_loss(self, generated_output: tf.Tensor, lambda_g: float, z_trigger: n
         aux_loss = tf.math.reduce_mean(tf.math.squared_difference(self._gan.generator.model(z_trigger), x_target))
         return orig_loss + lambda_g * aux_loss
 
-    @tf.function
     def fidelity(self, z_trigger: np.ndarray, x_target: np.ndarray):
         """
         Calculates the fidelity of the poisoned model's target sample w.r.t. the original x_target sample
+
         :param z_trigger: the secret backdoor trigger that will produce the target
         :param x_target: the target to produce when using the trigger
         """
@@ -98,6 +101,7 @@ def poison_estimator(
     ) -> "GENERATOR_TYPE":
         """
         Creates a backdoor in the generative model
+
         :param z_trigger: the secret backdoor trigger that will produce the target
         :param x_target: the target to produce when using the trigger
         :param batch_size: batch_size of images used to train generator

art/estimators/gan/__init__.py

@@ -1,4 +1,4 @@
 """
 GAN Estimator API.
 """
-from art.estimators.gan.tensorflow_gan import TensorFlow2GAN
+from art.estimators.gan.tensorflow import TensorFlowV2GAN

art/estimators/gan/tensorflow_gan.py → art/estimators/gan/tensorflow.py

@@ -18,18 +18,19 @@
 """
 This module creates GANs using the TensorFlow ML Framework
 """
-from typing import Any, Tuple, TYPE_CHECKING
+from typing import Tuple, TYPE_CHECKING, Union
+
 import numpy as np
-import tensorflow as tf
-from art.estimators.estimator import BaseEstimator
+from art.estimators.tensorflow import TensorFlowV2Estimator
 
 if TYPE_CHECKING:
     from art.utils import CLASSIFIER_TYPE, GENERATOR_TYPE
+    import tensorflow as tf
 
 
-class TensorFlow2GAN(BaseEstimator):
+class TensorFlowV2GAN(TensorFlowV2Estimator):
     """
-    This class implements a GAN with the TensorFlow framework.
+    This class implements a GAN with the TensorFlow v2 framework.
     """
 
     def __init__(
@@ -42,29 +43,32 @@ def __init__(
         discriminator_optimizer_fct=None,
     ):
         """
-        Initialization of a test TF2 GAN
+        Initialization of a test TensorFlow v2 GAN
+
         :param generator: a TensorFlow2 generator
-        :param discriminator: a TensorFlow 2 discriminator
+        :param discriminator: a TensorFlow v2 discriminator
         :param generator_loss: the loss function to use for the generator
         :param discriminator_loss: the loss function to use for the discriminator
         :param generator_optimizer_fct: the optimizer function to use for the generator
         :param discriminator_optimizer_fct: the optimizer function to use for the discriminator
         """
-        super().__init__(model=None, clip_values=None)
+        super().__init__(model=None, clip_values=None, channels_first=None)
         self._generator = generator
         self._discriminator_classifier = discriminator
         self._generator_loss = generator_loss
         self._generator_optimizer_fct = generator_optimizer_fct
         self._discriminator_loss = discriminator_loss
         self._discriminator_optimizer_fct = discriminator_optimizer_fct
 
-    def predict(self, x: np.ndarray, **kwargs) -> Any:  # lgtm [py/inheritance/incorrect-overridden-signature]
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
         """
-        Generates a sample
-        param x: a seed
-        :return: the sample
+        Generates a sample.
+
+        :param x: A input seed.
+        :param batch_size: The batch size for predictions.
+        :return: The generated sample.
         """
-        return self.generator.model(x, training=False)
+        return self.generator.predict(x, batch_size=batch_size, **kwargs)
 
     @property
     def input_shape(self) -> Tuple[int, int]:
@@ -75,25 +79,19 @@ def input_shape(self) -> Tuple[int, int]:
         """
         return 1, 100
 
-    def fit(self, x: np.ndarray, y: np.ndarray, **kwargs) -> None:
+    def fit(self, x: np.ndarray, y: np.ndarray, batch_size: int = 128, nb_epochs: int = 20, **kwargs) -> None:
         """
         Creates a generative model
 
         :param x: the secret backdoor trigger that will produce the target
         :param y: the target to produce when using the trigger
         :param batch_size: batch_size of images used to train generator
-        :param max_iter: total number of iterations for performing the attack
+        :param nb_epochs: total number of iterations for performing the attack
         """
-        max_iter = kwargs.get("max_iter")
-        if max_iter is None:
-            raise ValueError("max_iter argument was None. The value must be a positive integer")
-
-        batch_size = kwargs.get("batch_size")
-        if batch_size is None:
-            raise ValueError("batch_size argument was None. The value must be a positive integer")
+        import tensorflow as tf  # lgtm [py/repeated-import]
 
         z_trigger = x
-        for _ in range(max_iter):
+        for _ in range(nb_epochs):
             train_imgs = kwargs.get("images")
             train_set = (
                 tf.data.Dataset.from_tensor_slices(train_imgs)
@@ -167,3 +165,11 @@ def discriminator_optimizer_fct(self) -> "tf.Tensor":
         :return: the optimizer function for the discriminator
         """
         return self._discriminator_optimizer_fct
+
+    def loss_gradient(self, x, y, **kwargs):
+        raise NotImplementedError
+
+    def get_activations(
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
+        raise NotImplementedError

art/estimators/generation/__init__.py

@@ -4,4 +4,4 @@
 from art.estimators.generation.generator import GeneratorMixin
 
 from art.estimators.generation.tensorflow import TensorFlowGenerator
-from art.estimators.generation.tensorflow import TensorFlow2Generator
+from art.estimators.generation.tensorflow import TensorFlowV2Generator

art/estimators/generation/tensorflow.py

@@ -23,12 +23,13 @@
 import logging
 from typing import Any, Dict, List, Optional, Union, Tuple, TYPE_CHECKING
 
+import numpy as np
+
 from art.estimators.generation.generator import GeneratorMixin
 from art.estimators.tensorflow import TensorFlowEstimator, TensorFlowV2Estimator
 
 if TYPE_CHECKING:
     # pylint: disable=C0412
-    import numpy as np
     import tensorflow.compat.v1 as tf
 
     from art.utils import CLIP_VALUES_TYPE, PREPROCESSING_TYPE
@@ -143,7 +144,7 @@ def feed_dict(self) -> Dict[Any, Any]:
         """
         return self._feed_dict  # type: ignore
 
-    def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs) -> "np.ndarray":
+    def predict(self, x: np.ndarray, batch_size: int = 128, **kwargs) -> np.ndarray:
         """
         Perform projections over a batch of encodings.
 
@@ -158,7 +159,7 @@ def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs) -> "np.ndarr
         y = self._sess.run(self._model, feed_dict=feed_dict)
         return y
 
-    def loss_gradient(self, x, y, training_mode: bool = False, **kwargs) -> "np.ndarray":  # pylint: disable=W0221
+    def loss_gradient(self, x, y, training_mode: bool = False, **kwargs) -> np.ndarray:  # pylint: disable=W0221
         raise NotImplementedError
 
     def fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs):
@@ -168,14 +169,14 @@ def fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs):
         raise NotImplementedError
 
     def get_activations(
-        self, x: "np.ndarray", layer: Union[int, str], batch_size: int, framework: bool = False
-    ) -> "np.ndarray":
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
         """
         Do nothing.
         """
         raise NotImplementedError
 
-    def compute_loss(self, x: "np.ndarray", y: "np.ndarray", **kwargs) -> "np.ndarray":
+    def compute_loss(self, x: np.ndarray, y: np.ndarray, **kwargs) -> np.ndarray:
         raise NotImplementedError
 
     @property
@@ -195,7 +196,7 @@ def encoding_length(self) -> int:
         return self._encoding_length
 
 
-class TensorFlow2Generator(GeneratorMixin, TensorFlowV2Estimator):  # lgtm [py/missing-call-to-init]
+class TensorFlowV2Generator(GeneratorMixin, TensorFlowV2Estimator):  # lgtm [py/missing-call-to-init]
     """
     This class implements a DGM with the TensorFlow framework.
     """
@@ -258,19 +259,35 @@ def encoding_length(self) -> int:
     def input_shape(self) -> Tuple[int, ...]:
         raise NotImplementedError
 
-    def predict(self, x: "np.ndarray", batch_size: int = 128, **kwargs) -> "np.ndarray":
+    def predict(  # pylint: disable=W0221
+        self, x: np.ndarray, batch_size: int = 128, training_mode: bool = False, **kwargs
+    ) -> np.ndarray:
         """
         Perform projections over a batch of encodings.
 
         :param x: Encodings.
         :param batch_size: Batch size.
+        :param training_mode: `True` for model set to training mode and `'False` for model set to evaluation mode.
         :return: Array of prediction projections of shape `(num_inputs, nb_classes)`.
         """
-        logging.info("Projecting new sample from z value")
-        y = self._model(x)
-        return y
+        # Run prediction with batch processing
+        results_list = []
+        num_batch = int(np.ceil(len(x) / float(batch_size)))
+        for m in range(num_batch):
+            # Batch indexes
+            begin, end = (
+                m * batch_size,
+                min((m + 1) * batch_size, x.shape[0]),
+            )
+
+            # Run prediction
+            results_list.append(self._model(x[begin:end], training=training_mode).numpy())
+
+        results = np.vstack(results_list)
+
+        return results
 
-    def loss_gradient(self, x, y, **kwargs) -> "np.ndarray":
+    def loss_gradient(self, x, y, **kwargs) -> np.ndarray:
         raise NotImplementedError
 
     def fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs):
@@ -280,8 +297,8 @@ def fit(self, x, y, batch_size=128, nb_epochs=10, **kwargs):
         raise NotImplementedError
 
     def get_activations(
-        self, x: "np.ndarray", layer: Union[int, str], batch_size: int, framework: bool = False
-    ) -> "np.ndarray":
+        self, x: np.ndarray, layer: Union[int, str], batch_size: int, framework: bool = False
+    ) -> np.ndarray:
         """
         Do nothing.
         """