Artifact Feature Purification for Cross-domain Detection of AI-generated Images

• URL: http://arxiv.org/abs/2403.11172v1
• Date: Sun, 17 Mar 2024 11:17:06 GMT
• Title: Artifact Feature Purification for Cross-domain Detection of AI-generated Images
• Authors: Zheling Meng, Bo Peng, Jing Dong, Tieniu Tan,
• Abstract summary: Existing generated image detection methods suffer from performance drop when faced with out-of-domain generators and image scenes. We propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For cross-generator detection, the average accuracy of APN is 5.6% 16.4% higher than the previous 10 methods on GenImage dataset and 1.7% 50.1% on DiffusionForensics dataset.
• Score: 38.18870936370117
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the era of AIGC, the fast development of visual content generation technologies, such as diffusion models, bring potential security risks to our society. Existing generated image detection methods suffer from performance drop when faced with out-of-domain generators and image scenes. To relieve this problem, we propose Artifact Purification Network (APN) to facilitate the artifact extraction from generated images through the explicit and implicit purification processes. For the explicit one, a suspicious frequency-band proposal method and a spatial feature decomposition method are proposed to extract artifact-related features. For the implicit one, a training strategy based on mutual information estimation is proposed to further purify the artifact-related features. Experiments show that for cross-generator detection, the average accuracy of APN is 5.6% ~ 16.4% higher than the previous 10 methods on GenImage dataset and 1.7% ~ 50.1% on DiffusionForensics dataset. For cross-scene detection, APN maintains its high performance. Via visualization analysis, we find that the proposed method extracts flexible forgery patterns and condenses the forgery information diluted in irrelevant features. We also find that the artifact features APN focuses on across generators and scenes are global and diverse. The code will be available on GitHub.

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