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Although numerous forensic algorithms, ranging from handcrafted methods to deep learning-based detectors, have been developed for manipulation detection, individual methods often suffer from limited robustness, fragmented evidence, or weak generalization across manipulation types and image conditions. To address these limitations, we present \\textbf{FRAME}, a method for \\textbf{F}orensic \\textbf{R}outing and \\textbf{A}daptive \\textbf{M}ulti-path \\textbf{E}vidence fusion for image manipulation detection. FRAME organizes diverse forensic algorithms into a multi-path analysis space, adaptively selects informative forensic paths for each input image, and fuses complementary evidence","title":"FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection","url":"https://arxiv.org/abs/2605.12826","vendor":"arxiv_cs_ai"},"summary":"arXiv:2605.12826v1 Announce Type: cross \nAbstract: The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital images increasingly challenging, with important implications for journalism, forensic analysis, and public trust. Although numerous forensic algorithms, ranging from handcrafted methods to deep learning-based detectors, have been developed for manipulation detection, individual methods often suffer from limited robustness, fragmented evidence, or weak generalization across manipulation types and image conditions. To address these limitations, we present \\textbf{FRAME}, a method for \\textbf{F}orensic \\textbf{R}outing and \\textbf{A}daptive \\textbf{M}ulti-path \\textbf{E}vidence fusion for image manipulation detection. 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