Data to 7 Oct 2026

C2P-CLIP: Injecting Category Common Prompt in CLIP to Enhance Generalization in Deepfake Detection

arXiv 2408.09647 ↗First posted 19 Aug 2024cs.CVcited by 111

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Abstract (arXiv)

This work focuses on AIGC detection to develop universal detectors capable of identifying various types of forgery images. Recent studies have found large pre-trained models, such as CLIP, are effective for generalizable deepfake detection along with linear classifiers. However, two critical issues remain unresolved: 1) understanding why CLIP features are effective on deepfake detection through a linear classifier; and 2) exploring the detection potential of CLIP. In this study, we delve into the underlying mechanisms of CLIP's detection capabilities by decoding its detection features into text and performing word frequency analysis. Our finding indicates that CLIP detects deepfakes by recognizing similar concepts (Fig. reffig:fig1 a). Building on this insight, we introduce Category Common Prompt CLIP, called C2P-CLIP, which integrates the category common prompt into the text encoder to inject category-related concepts into the image encoder, thereby enhancing detection performance (Fig. reffig:fig1 b). Our method achieves a 12.41% improvement in detection accuracy compared to the original CLIP, without introducing additional parameters during testing. Comprehensive experiments conducted on two widely-used datasets, encompassing 20 generation models, validate the efficacy of the proposed method, demonstrating state-of-the-art performance. The code is available at urlhttps://github.com/chuangchuangtan/C2P-CLIP-DeepfakeDetection

Key facts. Cited by 111 papers in the map (data through 7 Oct 2026). Builds on 49 of its 65 references that are in the map. First posted on arXiv 19 Aug 2024.

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49 of 65 references.

References · 49

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Citing papers per year: 2024: 5; 2025: 47; 2026: 59.

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2024A Bias-Free Training Paradigm for More General AI-generated Image Detectioncited by 67
2025Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizablecited by 58
2025Is Artificial Intelligence Generated Image Detection a Solved Problem?cited by 49
2025CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AIcited by 42
2025Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarioscited by 24
2025All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learningcited by 23
2024FSFM: A Generalizable Face Security Foundation Model via Self-Supervised Facial Representation Learningcited by 21
2025Toward Generalizable Forgery Detection and Reasoningcited by 20
2025Breaking Latent Prior Bias in Detectors for Generalizable AIGC Image Detectioncited by 16
2024ForgeLens: Data-Efficient Forgery Focus for Generalizable Forgery Image Detectioncited by 14
2025Few-Shot Learner Generalizes Across AI-Generated Image Detectioncited by 13
2025Survey on AI-Generated Media Detection: From Non-MLLM to MLLMcited by 12

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