Inference Time Evidences of Adversarial Attacks for Forensic on Transformers

TL;DR

This paper investigates inference-time signatures in Vision Transformers to detect adversarial attacks, proposing quantifications of inputs and outputs that distinguish clean from adversarial samples effectively.

Contribution

It introduces four novel quantifications of ViT inputs, outputs, and latent features for adversarial attack detection during inference.

Findings

Input and output quantifications effectively distinguish adversarial samples.

Latent features provide limited but insightful information about adversarial perturbations.

Proposed signatures show promise for real-time forensic detection of attacks.

Abstract

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.