Inference Time Evidences of Adversarial Attacks for Forensic on Transformers
This work addresses security vulnerabilities in Vision Transformers for computer vision applications, but it is incremental as it builds on existing detection methods.
The paper tackled the problem of detecting adversarial attacks on Vision Transformers during inference by designing four quantifications based on input, output, and latent features, finding that input and output quantifications effectively distinguish clean from adversarial samples.
Vision Transformers (ViTs) are becoming a very popular paradigm for vision tasks as they achieve state-of-the-art performance on image classification. However, although early works implied that this network structure had increased robustness against adversarial attacks, some works argue ViTs are still vulnerable. This paper presents our first attempt toward detecting adversarial attacks during inference time using the network's input and outputs as well as latent features. We design four quantifications (or derivatives) of input, output, and latent vectors of ViT-based models that provide a signature of the inference, which could be beneficial for the attack detection, and empirically study their behavior over clean samples and adversarial samples. The results demonstrate that the quantifications from input (images) and output (posterior probabilities) are promising for distinguishing clean and adversarial samples, while latent vectors offer less discriminative power, though they give some insights on how adversarial perturbations work.