Paper
When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection
Authors
Chao Shuai, Zhenguang Liu, Shaojing Fan, Bin Gong, Weichen Lian, Xiuli Bi, Zhongjie Ba, Kui Ren
Abstract
AI-generated image detection has become increasingly important with the rapid advancement of generative AI. However, detectors built on Vision Foundation Models (VFMs, \emph{e.g.}, CLIP) often struggle to generalize to images created using unseen generation pipelines. We identify, for the first time, a key failure mechanism, termed \emph{semantic fallback}, where VFM-based detectors rely on dominant pre-trained semantic priors (such as identity) rather than forgery-specific traces under distribution shifts. To address this issue, we propose \textbf{Geometric Semantic Decoupling (GSD)}, a parameter-free module that explicitly removes semantic components from learned representations by leveraging a frozen VFM as a semantic guide with a trainable VFM as an artifact detector. GSD estimates semantic directions from batch-wise statistics and projects them out via a geometric constraint, forcing the artifact detector to rely on semantic-invariant forensic evidence. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches, achieving 94.4\% video-level AUC (+\textbf{1.2\%}) in cross-dataset evaluation, improving robustness to unseen manipulations (+\textbf{3.0\%} on DF40), and generalizing beyond faces to the detection of synthetic images of general scenes, including UniversalFakeDetect (+\textbf{0.9\%}) and GenImage (+\textbf{1.7\%}).
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.09242v1</id>\n <title>When Detectors Forget Forensics: Blocking Semantic Shortcuts for Generalizable AI-Generated Image Detection</title>\n <updated>2026-03-10T06:16:35Z</updated>\n <link href='https://arxiv.org/abs/2603.09242v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.09242v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>AI-generated image detection has become increasingly important with the rapid advancement of generative AI. However, detectors built on Vision Foundation Models (VFMs, \\emph{e.g.}, CLIP) often struggle to generalize to images created using unseen generation pipelines. We identify, for the first time, a key failure mechanism, termed \\emph{semantic fallback}, where VFM-based detectors rely on dominant pre-trained semantic priors (such as identity) rather than forgery-specific traces under distribution shifts. To address this issue, we propose \\textbf{Geometric Semantic Decoupling (GSD)}, a parameter-free module that explicitly removes semantic components from learned representations by leveraging a frozen VFM as a semantic guide with a trainable VFM as an artifact detector. GSD estimates semantic directions from batch-wise statistics and projects them out via a geometric constraint, forcing the artifact detector to rely on semantic-invariant forensic evidence. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art approaches, achieving 94.4\\% video-level AUC (+\\textbf{1.2\\%}) in cross-dataset evaluation, improving robustness to unseen manipulations (+\\textbf{3.0\\%} on DF40), and generalizing beyond faces to the detection of synthetic images of general scenes, including UniversalFakeDetect (+\\textbf{0.9\\%}) and GenImage (+\\textbf{1.7\\%}).</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n <published>2026-03-10T06:16:35Z</published>\n <arxiv:primary_category term='cs.CV'/>\n <author>\n <name>Chao Shuai</name>\n </author>\n <author>\n <name>Zhenguang Liu</name>\n </author>\n <author>\n <name>Shaojing Fan</name>\n </author>\n <author>\n <name>Bin Gong</name>\n </author>\n <author>\n <name>Weichen Lian</name>\n </author>\n <author>\n <name>Xiuli Bi</name>\n </author>\n <author>\n <name>Zhongjie Ba</name>\n </author>\n <author>\n <name>Kui Ren</name>\n </author>\n </entry>"
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