2022-11-01 更新
Universalization of any adversarial attack using very few test examples
Authors:Sandesh Kamath, Amit Deshpande, K V Subrahmanyam, Vineeth N Balasubramanian
Deep learning models are known to be vulnerable not only to input-dependent adversarial attacks but also to input-agnostic or universal adversarial attacks. Dezfooli et al. \cite{Dezfooli17,Dezfooli17anal} construct universal adversarial attack on a given model by looking at a large number of training data points and the geometry of the decision boundary near them. Subsequent work \cite{Khrulkov18} constructs universal attack by looking only at test examples and intermediate layers of the given model. In this paper, we propose a simple universalization technique to take any input-dependent adversarial attack and construct a universal attack by only looking at very few adversarial test examples. We do not require details of the given model and have negligible computational overhead for universalization. We theoretically justify our universalization technique by a spectral property common to many input-dependent adversarial perturbations, e.g., gradients, Fast Gradient Sign Method (FGSM) and DeepFool. Using matrix concentration inequalities and spectral perturbation bounds, we show that the top singular vector of input-dependent adversarial directions on a small test sample gives an effective and simple universal adversarial attack. For VGG16 and VGG19 models trained on ImageNet, our simple universalization of Gradient, FGSM, and DeepFool perturbations using a test sample of 64 images gives fooling rates comparable to state-of-the-art universal attacks \cite{Dezfooli17,Khrulkov18} for reasonable norms of perturbation. Code available at https://github.com/ksandeshk/svd-uap .
PDF Appeared in ACM CODS-COMAD 2022 (Research Track)
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Improving Transferability of Adversarial Examples on Face Recognition with Beneficial Perturbation Feature Augmentation
Authors:Fengfan Zhou, Hefei Ling, Yuxuan Shi, Jiazhong Chen, Zongyi Li, Qian Wang
Face recognition (FR) models can be easily fooled by adversarial examples, which are crafted by adding imperceptible perturbations on benign face images. To improve the transferability of adversarial examples on FR models, we propose a novel attack method called Beneficial Perturbation Feature Augmentation Attack (BPFA), which reduces the overfitting of the adversarial examples to surrogate FR models by the adversarial strategy. Specifically, in the backpropagation step, BPFA records the gradients on pre-selected features and uses the gradient on the input image to craft adversarial perturbation to be added on the input image. In the next forward propagation step, BPFA leverages the recorded gradients to add perturbations(i.e., beneficial perturbations) that can be pitted against the adversarial perturbation added on the input image on their corresponding features. The above two steps are repeated until the last backpropagation step before the maximum number of iterations is reached. The optimization process of the adversarial perturbation added on the input image and the optimization process of the beneficial perturbations added on the features correspond to a minimax two-player game. Extensive experiments demonstrate that BPFA outperforms the state-of-the-art gradient-based adversarial attacks on FR.
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Improving Hyperspectral Adversarial Robustness using Ensemble Networks in the Presences of Multiple Attacks
Authors:Nicholas Soucy, Salimeh Yasaei Sekeh
Semantic segmentation of hyperspectral images (HSI) has seen great strides in recent years by incorporating knowledge from deep learning RGB classification models. Similar to their classification counterparts, semantic segmentation models are vulnerable to adversarial examples and need adversarial training to counteract them. Traditional approaches to adversarial robustness focus on training or retraining a single network on attacked data, however, in the presence of multiple attacks these approaches decrease the performance compared to networks trained individually on each attack. To combat this issue we propose an Adversarial Discriminator Ensemble Network (ADE-Net) which focuses on attack type detection and adversarial robustness under a unified model to preserve per data-type weight optimally while robustifiying the overall network. In the proposed method, a discriminator network is used to separate data by attack type into their specific attack-expert ensemble network. Our approach allows for the presence of multiple attacks mixed together while also labeling attack types during testing. We experimentally show that ADE-Net outperforms the baseline, which is a single network adversarially trained under a mix of multiple attacks, for HSI Indian Pines, Kennedy Space, and Houston datasets.
PDF 6 pages, 2 figures, 1 table, 1 algorithm
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