Improving the JPEG-resistance of Adversarial Attacks on Face Recognition
  by Interpolation Smoothing
Kefu Guo Fengfan Zhou Hefei Ling Ping Li Hui Liu
JPEG compression can significantly impair the performance of adversarial face
examples, which previous adversarial attacks on face recognition (FR) have not
adequately addressed. Considering this challenge, we propose a novel
adversarial attack on FR that aims to improve the resistance of adversarial
examples against JPEG compression. Specifically, during the iterative process
of generating adversarial face examples, we interpolate the adversarial face
examples into a smaller size. Then we utilize these interpolated adversarial
face examples to create the adversarial examples in the next iteration.
Subsequently, we restore the adversarial face examples to their original size
by interpolating. Throughout the entire process, our proposed method can smooth
the adversarial perturbations, effectively mitigating the presence of
high-frequency signals in the crafted adversarial face examples that are
typically eliminated by JPEG compression. Our experimental results demonstrate
the effectiveness of our proposed method in improving the JPEG-resistance of
adversarial face examples.