Merge pull request #2440 from CNOCycle/attack/snal

Added support for SNAL attack

Author: beat-buesser

art/attacks/evasion/__init__.py

@@ -63,6 +63,7 @@
 from art.attacks.evasion.shapeshifter import ShapeShifter
 from art.attacks.evasion.simba import SimBA
 from art.attacks.evasion.spatial_transformation import SpatialTransformation
+from art.attacks.evasion.steal_now_attack_later.steal_now_attack_later import SNAL
 from art.attacks.evasion.square_attack import SquareAttack
 from art.attacks.evasion.pixel_threshold import ThresholdAttack
 from art.attacks.evasion.universal_perturbation import UniversalPerturbation

art/attacks/evasion/steal_now_attack_later/__init__.py

@@ -0,0 +1,70 @@
+# pylint: disable=C0114
+# BSD 3-Clause License
+#
+# Copyright (c) Soumith Chintala 2016,
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# * Redistributions of source code must retain the above copyright notice, this
+#  list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright notice,
+#  this list of conditions and the following disclaimer in the documentation
+#  and/or other materials provided with the distribution.
+#
+# * Neither the name of the copyright holder nor the names of its
+#  contributors may be used to endorse or promote products derived from
+#  this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import torch
+
+
+def drop_block2d(x: "torch.Tensor", prob: float, block_size: int):
+    """
+    === NOTE ===
+    This function is modified from torchvision (torchvision/ops/drop_block.py)
+    BSD 3-Clause License
+    === ==== ===
+    :param x (Tensor[N, C, H, W]): The input tensor or 4-dimensions with the first one
+                   being its batch i.e. a batch with ``N`` rows.
+    :param prob (float): Probability of an element to be dropped.
+    :param block_size (int): Size of the block to drop.
+
+    :return: Tensor[N, C, H, W]: The mask of activate pixels.
+    """
+    import torch
+
+    if prob < 0.0 or prob > 1.0:
+        raise ValueError(f"drop probability has to be between 0 and 1, but got {prob}.")
+    if x.ndim != 4:
+        raise ValueError(f"input should be 4 dimensional. Got {x.ndim} dimensions.")
+
+    N, _, H, W = x.size()  # pylint: disable=C0103
+    block_size = min(block_size, W, H)
+    # compute the gamma of Bernoulli distribution
+    gamma = (prob * H * W) / ((block_size**2) * ((H - block_size + 1) * (W - block_size + 1)))
+    noise = torch.empty((N, 1, H - block_size + 1, W - block_size + 1), dtype=x.dtype, device=x.device)
+    noise.bernoulli_(gamma)
+
+    noise = torch.nn.functional.pad(noise, [block_size // 2] * 4, value=0)
+    noise = torch.nn.functional.max_pool2d(
+        noise, stride=(1, 1), kernel_size=(block_size, block_size), padding=block_size // 2
+    )
+    mask = 1 - noise
+    return mask