Abstract
It has been observed that the unauthorized use of face recognition system raises privacy problems. Using adversarial perturbations provides one possible solution to address this issue. A critical issue to exploit adversarial perturbation against unauthorized face recognition system is that: The images uploaded to the web need to be processed by JPEG compression, which weakens the effectiveness of adversarial perturbation. Existing JPEG compression-resistant methods fails to achieve a balance among compression resistance, transferability, and attack effectiveness. To this end, we propose a more natural solution called low frequency adversarial perturbation (LFAP). Instead of restricting the adversarial perturbations, we turn to regularize the source model to employing more low-frequency features by adversarial training. Moreover, to better influence model in different frequency components, we proposed the refined low-mid frequency adversarial perturbation (LMFAP) considering the mid frequency components as the productive complement. We designed a variety of settings in this study to simulate the real-world application scenario, including cross backbones, supervisory heads, training datasets and testing datasets. Quantitative and qualitative experimental results validate the effectivenss of proposed solutions.
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URL
https://arxiv.org/abs/2206.09410
