Label Universal Targeted Attack
This addresses security vulnerabilities in deep learning models for applications like image recognition, though it is incremental as it builds on existing adversarial attack methods.
The paper tackles the problem of targeted adversarial attacks by introducing LUTA, which forces a deep model to predict an attacker-chosen label for any sample of a given source class with high probability, achieving high fooling ratios on ImageNet and VGGFace models and demonstrating transferability to the physical world.
We introduce Label Universal Targeted Attack (LUTA) that makes a deep model predict a label of attacker's choice for `any' sample of a given source class with high probability. Our attack stochastically maximizes the log-probability of the target label for the source class with first order gradient optimization, while accounting for the gradient moments. It also suppresses the leakage of attack information to the non-source classes for avoiding the attack suspicions. The perturbations resulting from our attack achieve high fooling ratios on the large-scale ImageNet and VGGFace models, and transfer well to the Physical World. Given full control over the perturbation scope in LUTA, we also demonstrate it as a tool for deep model autopsy. The proposed attack reveals interesting perturbation patterns and observations regarding the deep models.