Pith. sign in

REVIEW 8 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2408.09647 v2 pith:6G7GE3D5 submitted 2024-08-19 cs.CV

Chuangchuang Tan , Renshuai Tao , Huan Liu , Guanghua Gu , Baoyuan Wu , Yao Zhao , Yunchao Wei This is my paper · ORCID
classification cs.CV
keywords detectionclipcategorycommondeepfakepromptc2p-clipconcepts
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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. \ref{fig: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. \ref{fig: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 \url{https://github.com/chuangchuangtan/C2P-CLIP-DeepfakeDetection}

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Bridging the Gap Between Ideal and Real-world Evaluation: Benchmarking AI-Generated Image Detection in Challenging Scenarios

    cs.CV 2025-09 conditional novelty 7.0 of 10

    AI-generated image detectors lose substantial accuracy on images shared over social media or scanned/re-photographed, while humans improve quickly after seeing two examples.

  2. Continuously Evolving Deepfake Detection: An Architecture and Public-Benchmark Evaluation of a Dynamic Detection System

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A continuously refreshed, incentive-driven deepfake detector beats static detectors on in-the-wild benchmarks and improves on post-export AI-generated media.

  3. VendorBench-100: A Unified Cross-Paradigm Benchmark for Deepfake Image Detection

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A 36-model cross-paradigm benchmark on a hard 100-image corpus shows commercial APIs lead on MCC, open-source detectors trail on average, and a subset of strong rankers are miscalibrated at their default threshold.

  4. RAVID: Retrieval-Augmented Visual Detection: A Knowledge-Driven Approach for AI-Generated Image Identification

    cs.CV 2025-08 conditional novelty 6.0 of 10

    RAVID detects AI-generated images by retrieving similar images from a database and feeding them to a vision-language model, reporting 93.85% average accuracy on UniversalFakeDetect.

  5. HAMLET-FFD: Hierarchical Adaptive Multi-modal Learning Embeddings Transformation for Face Forgery Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A frozen-CLIP plugin with bidirectional visual-text fusion reaches 90.07% average AUC on seven unseen deepfake benchmarks, up 6.68 points over prior work.

  6. ForenX: Towards Explainable AI-Generated Image Detection with Multimodal Large Language Models

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    ForenX detects AI-generated images with MLLMs guided by a forensic prompt and trained on a new explanation dataset, ForgReason.

  7. Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems

    cs.CR 2025-07 conditional novelty 3.0 of 10

    A systematic review of deepfake detection finds a pervasive lack of adversarial robustness evaluation across all modalities and calls for resilient, modality-agnostic detectors.

  8. Survey on AI-Generated Media Detection: From Non-MLLM to MLLM

    cs.CV 2025-02 unverdicted novelty 3.0 of 10

    A survey organizing AI-generated media detection into Non-MLLM and MLLM based methods, with task and benchmark taxonomies.

Pith tools