Abstract
The rapid advancement of generative image technology has introduced significant security concerns, particularly in the domain of face generation detection. This paper investigates the vulnerabilities of current AI-generated face detection systems. Our study reveals that while existing detection methods often achieve high accuracy under standard conditions, they exhibit limited robustness against adversarial attacks. To address these challenges, we propose an approach that integrates adversarial training to mitigate the impact of adversarial examples. Furthermore, we utilize diffusion inversion and reconstruction to further enhance detection robustness. Experimental results demonstrate that minor adversarial perturbations can easily bypass existing detection systems, but our method significantly improves the robustness of these systems. Additionally, we provide an in-depth analysis of adversarial and benign examples, offering insights into the intrinsic characteristics of AI-generated content. All associated code will be made publicly available in a dedicated repository to facilitate further research and verification.
Abstract (translated)
生成图像技术的迅速发展带来了显著的安全隐患，特别是在人脸生成检测领域。本文研究了当前人工智能生成的人脸检测系统的漏洞。我们的研究表明，在标准条件下，现有的检测方法通常能实现高精度，但在对抗性攻击面前则表现出有限的鲁棒性。为应对这些挑战，我们提出了一种结合对抗训练的方法，以减轻对抗样例的影响。此外，我们利用扩散逆向和重构技术进一步增强检测系统的稳健性。实验结果表明，轻微的对抗性扰动即可轻易绕过现有的检测系统，而我们的方法则显著提升了这些系统的鲁棒性。另外，我们对对抗性和良性样本进行了深入分析，提供了关于AI生成内容内在特性的见解。所有相关代码将通过一个专用仓库公开发布，以促进进一步的研究和验证。
URL
https://arxiv.org/abs/2505.03435
