Merge pull request #1446 from Trusted-AI/development_maintenance_190

Code maintenance updates for ART 1.9.0

Author: beat-buesser

art/attacks/evasion/__init__.py

@@ -28,6 +28,7 @@
 from art.attacks.evasion.imperceptible_asr.imperceptible_asr import ImperceptibleASR
 from art.attacks.evasion.imperceptible_asr.imperceptible_asr_pytorch import ImperceptibleASRPyTorch
 from art.attacks.evasion.iterative_method import BasicIterativeMethod
+from art.attacks.evasion.laser_attack.laser_attack import LaserAttack
 from art.attacks.evasion.lowprofool import LowProFool
 from art.attacks.evasion.newtonfool import NewtonFool
 from art.attacks.evasion.pe_malware_attack import MalwareGDTensorFlow

art/attacks/evasion/adversarial_texture/adversarial_texture_pytorch.py

@@ -324,6 +324,7 @@ def generate(  # type: ignore  # pylint: disable=W0222
         :param x: Input videos of shape NFHWC.
         :param y: True labels of format `List[Dict[str, np.ndarray]]`, one dictionary for each input image. The keys of
                   the dictionary are:
+
                   - boxes [N_FRAMES, 4]: the boxes in [x1, y1, x2, y2] format, with 0 <= x1 < x2 <= W and
                                          0 <= y1 < y2 <= H.
 

art/attacks/evasion/laser_attack/algorithms.py

@@ -55,7 +55,6 @@ def greedy_search(
 
     params = adv_object_generator.random()
     for _ in range(iterations):
-        predicted_class = actual_class
         for sign in [-1, 1]:
             params_prim = adv_object_generator.update_params(params, sign=sign)
             adversarial_image = image_generator.update_image(image, params_prim)

art/attacks/evasion/laser_attack/laser_attack.py

@@ -68,7 +68,7 @@ def __init__(
         """
         :param estimator: Predictor of the image class.
         :param iterations: Maximum number of iterations of the algorithm.
-        :param laser_generator: Object responsible for generation laser beams images and their updation.
+        :param laser_generator: Object responsible for generation laser beams images and their update.
         :param image_generator: Object responsible for image generation.
         :param random_initializations: How many times repeat the attack.
         :param optimisation_algorithm: Algorithm used to generate adversarial example. May be replaced.
@@ -280,7 +280,7 @@ def __init__(self, min_params: LaserBeam, max_params: LaserBeam, max_step: float
         """
         :params min_params: left bound of the params range
         :params max_params: right bound of the params range
-        :params max_step: maximal part of the random LaserBeam object drawed from the range.
+        :params max_step: maximal part of the random LaserBeam object drawn from the range.
         """
         self.min_params = min_params
         self.max_params = max_params

art/attacks/evasion/laser_attack/utils.py

@@ -20,26 +20,28 @@
 
 | Paper link: https://arxiv.org/abs/2103.06504
 """
-
-import string
 from abc import ABC, abstractmethod
-from dataclasses import dataclass
 from logging import Logger
 from pathlib import Path
+import string
 from typing import Any, Callable, List, Tuple, Union
 
 import numpy as np
 import matplotlib.pyplot as plt
 
 
-@dataclass
 class Line:
     """
     Representation of the linear function.
     """
 
-    angle: float
-    bias: float
+    def __init__(self, angle: float, bias: float):
+        """
+        :param angle: Angle in radian.
+        :param bias: Bias of the angle.
+        """
+        self.angle = angle
+        self.bias = bias
 
     def __call__(self, x: float) -> float:
         return np.math.tan(self.angle) * x + self.bias
@@ -64,14 +66,18 @@ def to_numpy(self) -> np.ndarray:
         return np.array([self.angle, self.bias])
 
 
-@dataclass
 class Range:
     """
     Representation of mathematical range concept
     """
 
-    left: float
-    right: float
+    def __init__(self, left: float, right: float):
+        """
+        :param left: Left range.
+        :param right: Right range.
+        """
+        self.left = left
+        self.right = right
 
     def __contains__(self, value):
         return self.left <= value < self.right
@@ -256,14 +262,18 @@ def save_nrgb_image(image: np.ndarray, number=0, name_length=5, directory="attac
     plt.imsave(im_name, image)
 
 
-@dataclass
 class DebugInfo:
     """
-    Logs debug informations during attacking process.
+    Logs debug information during attacking process.
     """
 
-    logger: Logger
-    artifacts_directory: str
+    def __init__(self, logger: Logger, artifacts_directory: str):
+        """
+        :param logger: Logger instance.
+        :param artifacts_directory: Artifacts directory.
+        """
+        self.logger = logger
+        self.artifacts_directory = artifacts_directory
 
     def log(self, adv_object: AdversarialObject) -> None:
         """

art/attacks/inference/membership_inference/label_only_boundary_distance.py

@@ -18,8 +18,8 @@
 """
 This module implements the Label-Only Inference Attack based on Decision Boundary.
 
-| Paper link: https://arxiv.org/abs/2007.14321 (Choquette-Choo et al.) and https://arxiv.org/abs/2007.15528 (Li and
-Zhang)
+| Paper link: https://arxiv.org/abs/2007.14321 (Choquette-Choo et al.)
+| Paper link: https://arxiv.org/abs/2007.15528 (Li and Zhang)
 """
 import logging
 from typing import Optional, TYPE_CHECKING
@@ -41,10 +41,10 @@ class LabelOnlyDecisionBoundary(MembershipInferenceAttack):
     """
     Implementation of Label-Only Inference Attack based on Decision Boundary.
 
-    | Paper link: https://arxiv.org/abs/2007.14321 (Choquette-Choo et al.) and https://arxiv.org/abs/2007.15528 (Li
-    and Zhang)
-
     You only need to call ONE of the calibrate methods, depending on which attack you want to launch.
+
+    | Paper link: https://arxiv.org/abs/2007.14321 (Choquette-Choo et al.)
+    | Paper link: https://arxiv.org/abs/2007.15528 (Li and Zhang)
     """
 
     attack_params = MembershipInferenceAttack.attack_params + [

art/estimators/object_tracking/__init__.py

@@ -1,4 +1,6 @@
 """
 Module containing estimators for object tracking.
 """
+from art.estimators.object_tracking.object_tracker import ObjectTrackerMixin
+
 from art.estimators.object_tracking.pytorch_goturn import PyTorchGoturn

art/estimators/poison_mitigation/__init__.py

@@ -3,3 +3,5 @@
 """
 from art.estimators.poison_mitigation import neural_cleanse
 from art.estimators.poison_mitigation.strip import strip
+from art.estimators.poison_mitigation.neural_cleanse.keras import KerasNeuralCleanse
+from art.estimators.poison_mitigation.strip.strip import STRIPMixin

conftest.py

@@ -235,7 +235,7 @@ def _get_image_iterator():
             return dataset
 
         if framework == "pytorch":
-            import torch
+            import torch  # lgtm [py/repeated-import]
 
             # Create tensors from data
             x_train_tens = torch.from_numpy(x_train_mnist)
@@ -946,7 +946,7 @@ def __init__(self):
                 preprocessing=(0, 1),
             )
 
-            import torch
+            import torch  # lgtm [py/repeated-import]
 
             self.channels_first = False
             self._input_shape = None