# AdvFaces: Adversarial Face Synthesis

## TL;DR

AdvFaces introduces an automated GAN-based method for creating imperceptible adversarial face images that effectively deceive face recognition systems with high success rates, addressing previous issues of poor perceptual quality and slow generation.

## Contribution

The paper presents AdvFaces, a novel GAN-based approach for rapid, high-quality adversarial face synthesis that significantly improves attack success rates against face recognition systems.

## Key findings

- Achieves up to 97.22% success in obfuscation attacks.
- Achieves up to 24.30% success in impersonation attacks.
- Generates imperceptible perturbations efficiently.

## Abstract

Face recognition systems have been shown to be vulnerable to adversarial examples resulting from adding small perturbations to probe images. Such adversarial images can lead state-of-the-art face recognition systems to falsely reject a genuine subject (obfuscation attack) or falsely match to an impostor (impersonation attack). Current approaches to crafting adversarial face images lack perceptual quality and take an unreasonable amount of time to generate them. We propose, AdvFaces, an automated adversarial face synthesis method that learns to generate minimal perturbations in the salient facial regions via Generative Adversarial Networks. Once AdvFaces is trained, it can automatically generate imperceptible perturbations that can evade state-of-the-art face matchers with attack success rates as high as 97.22% and 24.30% for obfuscation and impersonation attacks, respectively.