GRRE: Leveraging G-Channel Removed Reconstruction Error for Robust Detection of AI-Generated Images

• URL: http://arxiv.org/abs/2601.02709v1
• Date: Tue, 06 Jan 2026 04:53:10 GMT
• Title: GRRE: Leveraging G-Channel Removed Reconstruction Error for Robust Detection of AI-Generated Images
• Authors: Shuman He, Xiehua Li, Xioaju Yang, Yang Xiong, Keqin Li,
• Abstract summary: G-channel Removed Reconstruction Error (GRRE) is a simple yet effective method that exploits this discrepancy for robust AI-generated image detection.<n> GRRE consistently achieves high detection accuracy across multiple generative models, including those unseen during training.<n>These results highlight the potential of channel-removal-based reconstruction as a powerful forensic tool for safeguarding image authenticity in the era of generative AI.
• Score: 21.903079107456424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress of generative models, particularly diffusion models and GANs, has greatly increased the difficulty of distinguishing synthetic images from real ones. Although numerous detection methods have been proposed, their accuracy often degrades when applied to images generated by novel or unseen generative models, highlighting the challenge of achieving strong generalization. To address this challenge, we introduce a novel detection paradigm based on channel removal reconstruction. Specifically, we observe that when the green (G) channel is removed from real images and reconstructed, the resulting reconstruction errors differ significantly from those of AI-generated images. Building upon this insight, we propose G-channel Removed Reconstruction Error (GRRE), a simple yet effective method that exploits this discrepancy for robust AI-generated image detection. Extensive experiments demonstrate that GRRE consistently achieves high detection accuracy across multiple generative models, including those unseen during training. Compared with existing approaches, GRRE not only maintains strong robustness against various perturbations and post-processing operations but also exhibits superior cross-model generalization. These results highlight the potential of channel-removal-based reconstruction as a powerful forensic tool for safeguarding image authenticity in the era of generative AI.

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