Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks

• URL: http://arxiv.org/abs/2505.03435v1
• Date: Tue, 06 May 2025 11:19:01 GMT
• Title: Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks
• Authors: Sun Haoxuan, Hong Yan, Zhan Jiahui, Chen Haoxing, Lan Jun, Zhu Huijia, Wang Weiqiang, Zhang Liqing, Zhang Jianfu,
• Abstract summary: This paper investigates the vulnerabilities of current AI-generated face detection systems.<n>We propose an approach that integrates adversarial training to mitigate the impact of adversarial examples.<n>We also provide an in-depth analysis of adversarial and benign examples, offering insights into the intrinsic characteristics of AI-generated content.
• Score: 4.179092469766417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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