Self-Adaptive Feature Fool

Abstract

Recently, deep neural networks (DNNs) are shown to be susceptible to data-agnostic quasi-imperceptible noises called Universal Adversarial Perturbations (UAPs). Moreover, the techniques to craft UAPs can be categorized into data-driven and data-free. However, data-free techniques craft UAPs without utilizing any data samples and therefore result in weaker attack capacity. In this paper, we propose a novel method to craft UAPs in the absence of data, via adaptively perturbing mid-layer outputs of the CNN. Based on our proposed self-adaptive attention mechanism, we explore the effects of feature correlation of the internal representations on generating UAPs for the first time. Experimental evaluation demonstrates that UAPs crafted by our Self-Adaptive Feature Fool (SAFF) approach achieve state-of-the-art performance in data-free scenarios.