Generating Transferable Adversarial Examples against Vision Transformers

Abstract

Vision transformers (ViTs) are prevailing among several visual recognition tasks, therefore drawing intensive interest in generating adversarial examples against them. Different from CNNs, ViTs enjoy unique architectures, e.g., self-attention and image-embedding, which are commonly-shared features among various types of transformer-based models. However, existing adversarial methods suffer from weak transferable attacking ability due to the overlook of these architectural features. To address the problem, we propose an Architecture-oriented Transferable Attacking (ATA) framework to generate transferable adversarial examples by activating the uncertain attention and perturbing the sensitive embedding.Specifically, we first locate the patch-wise attentional regions that mostly affect model perception, therefore intensively activating the uncertainty of the attention mechanism and confusing the model decisions in turn.Furthermore, we search the pixel-wise attacking positions that are more likely to derange the embedded tokens using sensitive embedding perturbation, which could serve as a strong transferable attacking pattern.By jointly confusing the unique yet widely-used architectural features among transformer-based models, we can activate strong attacking transferability among diverse ViTs. Extensive experiments on large-scale dataset ImageNet using various popular transformers demonstrate that our ATA outperforms other baselines by large margins (at least +15% Attack Success Rate). Our code is available at https://github.com/nlsde-safety-team/ATA