Zero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis
arXiv:2608.20558v1 Announce Type: new Abstract: Digital cameras embed device-specific artifacts into every acquired image through demosaicing, in-camera post-processing, and lossy compression. These traces constitute a forensic signal that can be exploited to assess image authenticity. Existing passive methods rely predominantly on the green channel of the Bayer residual, discarding the correlated information available in the remaining color channels and typically requiring training data or device enrollment. This work proposes a zero-shot, training-free blind image manipulation localization pipeline that estimates a reference artifact pattern directly from the noise residual of a single suspect image, without assuming a fixed filter configuration, color layout, or block period. The pipeline incorporates a principled denoiser selection criterion based on the acquired-to-interpolated noise variance ratio, a block-level correlation analysis against the estimated reference pattern, and a two-component Gaussian Mixture Model scoring stage that produces a pixel-level tampering probability map. An ablation study evaluates the impact of denoiser choice and block size on localization accuracy, and comparisons against state-of-the-art passive methods demonstrate the competitiveness of the proposed zero-shot approach.
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[Submitted on 20 Aug 2026]
Title:Zero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis
View a PDF of the paper titled Zero-Shot Color Image Manipulation Localization via Noise Residual Artifact Pattern Analysis, by Edgar Gonzalez-Fernandez
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Abstract:Digital cameras embed device-specific artifacts into every acquired image through demosaicing, in-camera post-processing, and lossy compression. These traces constitute a forensic signal that can be exploited to assess image authenticity. Existing passive methods rely predominantly on the green channel of the Bayer residual, discarding the correlated information available in the remaining color channels and typically requiring training data or device enrollment. This work proposes a zero-shot, training-free blind image manipulation localization pipeline that estimates a reference artifact pattern directly from the noise residual of a single suspect image, without assuming a fixed filter configuration, color layout, or block period. The pipeline incorporates a principled denoiser selection criterion based on the acquired-to-interpolated noise variance ratio, a block-level correlation analysis against the estimated reference pattern, and a two-component Gaussian Mixture Model scoring stage that produces a pixel-level tampering probability map. An ablation study evaluates the impact of denoiser choice and block size on localization accuracy, and comparisons against state-of-the-art passive methods demonstrate the competitiveness of the proposed zero-shot approach.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2608.20558 [cs.CV]
(or arXiv:2608.20558v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2608.20558
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Edgar Gonzalez-Fernandez [view email] [v1] Thu, 20 Aug 2026 20:37:21 UTC (3,219 KB)
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