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Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks

Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein

University of Tübingen and EPFL

Paper: https://arxiv.org/abs/2006.12834

A short version is accepted to ECCV'20 Workshop on Adversarial Robustness in the Real World

Abstract

Sparse adversarial perturbations received much less attention in the literature compared to L2- and Linf-attacks. However, it is equally important to accurately assess the robustness of a model against sparse perturbations. Motivated by this goal, we propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: L0-bounded perturbations, adversarial patches, and adversarial frames. Unlike existing methods, the L0-version of untargeted Sparse-RS achieves almost 100% success rate on ImageNet by perturbing only 0.1% of the total number of pixels, outperforming all existing white-box attacks including L0-PGD. Moreover, our untargeted Sparse-RS achieves very high success rates even for the challenging settings of 20x20 adversarial patches and 2-pixel wide adversarial frames for 224x224 images. Finally, we show that Sparse-RS can be applied for universal adversarial patches where it significantly outperforms transfer-based approaches.

About the paper

Our proposed Sparse-RS framework is based on random search. Its main advantages are its simplicity and its wide applicability to multiple threat models:

We illustrate the versatility of the Sparse-RS framework by generating various sparse perturbations: L0-bounded, adversarial patches, and adversarial frames:

Sparse-RS also can successfully generate black-box universal attacks in sparse threat models without requiring a surrogate model:

In all these threat models, Sparse-RS improves over the existing approaches:

Moreover, for L0-perturbations Sparse-RS can even outperform existing white-box methods such as L0 PGD.

Code of Sparse-RS

The code is tested under Python 3.8.5 and PyTorch 1.8.0. It automatically downloads the pretrained models (either VGG-16-BN or ResNet-50) and requires access to ImageNet validation set.

The following are examples of how to run the attacks in the different threat models.

L0-bounded

In this case k represents the number of pixels to modify. For untargeted attacks

CUDA_VISIBLE_DEVICES=0 python eval.py --norm=L0 \
	--model=[pt_vgg | pt_resnet] --n_queries=10000 --alpha_init=0.3 \
	--data_path=/path/to/validation/set --k=150 --n_ex=500

and for targeted attacks please use --targeted --n_queries=100000 --alpha_init=0.1. The target class is randomly chosen for each point.

As additional options the flag --constant_schedule uses a constant schedule for alpha instead of the piecewise constant decreasing one, while with --seed=N it is possible to set a custom random seed.

Image-specific patches and frames

For untargeted image- and location-specific patches of size 20x20 (with k=400)

CUDA_VISIBLE_DEVICES=0 python eval.py --norm=patches \
	--model=[pt_vgg | pt_resnet] --n_queries=10000 --alpha_init=0.4 \
	--data_path=/path/to/validation/set --k=400 --n_ex=100

For targeted patches (size 40x40) please use --targeted --n_queries=50000 --alpha_init=0.1 --k=1600. The target class is randomly chosen for each point.

For untargeted image-specific frames of width 2 pixels (with k=2)

CUDA_VISIBLE_DEVICES=0 python eval.py --norm=frames \
	--model=[pt_vgg | pt_resnet] --n_queries=10000 --alpha_init=0.5 \
	--data_path=/path/to/validation/set --k=2 --n_ex=100

For targeted frames (width of 3 pixels) please use --targeted --n_queries=50000 --alpha_init=0.5 --k=3. The target class is randomly chosen for each point.

Universal patches and frames

For targeted universal patches of size 50x50 (with k=2500)

CUDA_VISIBLE_DEVICES=0 python eval.py \
	--norm=patches_universal --model=[pt_vgg | pt_resnet] \
	--n_queries=100000 --alpha_init=0.3 \
	--data_path=/path/to/validation/set --k=2500 \
	--n_ex=30 --targeted --target_class=530

and for targeted universal frames of width 6 pixels (k=6)

CUDA_VISIBLE_DEVICES=0 python eval.py \
	--norm=frames_universal --model=[pt_vgg | pt_resnet] \
	--n_queries=100000 --alpha_init=1.667 \
	--data_path=/path/to/validation/set --k=6 \
	--n_ex=30 --targeted --target_class=530

The argument --target_class specifies the number corresponding to the target label. To generate universal attacks we use batches of 30 images resampled every 10000 queries.

Visualizing resulting images

We provide a script vis_images.py to visualize the images produced by the attacks. To use it please run

python vis_images --path_data=/path/to/saved/results