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Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?

Yonggan Fu, Shunyao Zhang, Shang Wu, Cheng Wan, Yingyan Lin

Accepted at ICLR 2022 [Paper Link].

Overview

  • We propose a new attack framework, dubbed Patch-Fool, aiming to fool the self-attention mechanism by attacking the basic component (i.e., a single patch) participating in self-attention calculations.

  • We evaluate the robustness of both ViTs and CNNs against our Patch-Fool attack with extensive experiments and find that ViTs are consistently less robust than CNNs across various attack settings.

  • We further benchmark the robustness of both ViTs and CNNs under two variants of Patch-Fool, i.e., Sparse Patch-Fool and Mild Patch-Fool, and discover that the perturbation density and the perturbation strength highly influence the robustness ranking between ViTs and CNNs, where our Patch-Fool is an extreme case of high perturbation density and strength.

Visualization

We visualize the adversarial examples generated by Patch-Fool’s variants below, including Patch-Fool with different number of perturbed patches (rows 2∼3), Sparse Patch-Fool with a total of 250 perturbed pixels distributed in different number of perturbed patches (rows 4∼6), and Mild Patch-Fool under L2 and Linf constraints (rows 7∼8). The corresponding robust accuracy is also annotated.

Code Usage

Our codes support the Patch-Fool attack on top of SOTA Vision Transformers (e.g., DeiT-Ti, DeiT-S, and DeiT-B) and CNNs on ImageNet validation dataset.

Prerequisites

The required packages are listed in env.txt.

Key parameters

--data_dir: Path to the ImageNet folder.

--dataset_size: Evaluate on a part of the whole dataset.

--patch_select: Select patches based on the saliency map, attention map, or random selection.

--num_patch: Number of perturbed patches.

--sparse_pixel_num: Total number of perturbed pixels in the whole image.

--attack_mode: Optimize Patch-Fool based on the final cross-entropy loss only, or consider both cross-entropy loss and the attention map.

--attn_select: Select patches based on which attention layer.

--mild_l_2: Add L2 constraints on perturbed pixels.

--mild_l_inf: Add Linf constraints on perturbed pixels.

Evaluate Patch-Fool

We provide the following examples to evaluate the three variants of Patch-Fool, i.e., the vanilla Patch-Fool, Sparse Patch-Fool, and Mild Patch-Fool:

  • To Evaluate vanilla Patch-Fool:
python main.py --network DeiT-T --patch_select Attn --num_patch 1 --sparse_pixel_num 0 --attack_mode Attention
  • To Evaluate Sparse Patch-Fool:
python main.py --network DeiT-T --patch_select Attn --num_patch 1 --sparse_pixel_num 250 --attack_mode Attention
  • To Evaluate Mild Patch-Fool with Linf constraints:
python main.py --network DeiT-T --patch_select Attn --num_patch 1 --sparse_pixel_num 0 --attack_mode Attention --mild_l_inf 0.1

Citation

@inproceedings{fu2021patch,
  title={Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?},
  author={Fu, Yonggan and Zhang, Shunyao and Wu, Shang and Wan, Cheng and Lin, Yingyan},
  booktitle={International Conference on Learning Representations},
  year={2021}
}