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AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors

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arxiv 2310.17419 v3 pith:KDKXAL36 submitted 2023-10-26 cs.CV

You-Ming Chang , Chen Yeh , Wei-Chen Chiu , Ning Yu This is my paper · ORCID
classification cs.CV
keywords deepfakedetectionmodelsimagefakeaccuracyantifakepromptgenerative
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Deep generative models can create remarkably photorealistic fake images while raising concerns about misinformation and copyright infringement, known as deepfake threats. Deepfake detection technique is developed to distinguish between real and fake images, where the existing methods typically learn classifiers in the image domain or various feature domains. However, the generalizability of deepfake detection against emerging and more advanced generative models remains challenging. In this paper, being inspired by the zero-shot advantages of Vision-Language Models (VLMs), we propose a novel approach called AntifakePrompt, using VLMs (e.g., InstructBLIP) and prompt tuning techniques to improve the deepfake detection accuracy over unseen data. We formulate deepfake detection as a visual question answering problem, and tune soft prompts for InstructBLIP to answer the real/fake information of a query image. We conduct full-spectrum experiments on datasets from a diversity of 3 held-in and 20 held-out generative models, covering modern text-to-image generation, image editing and adversarial image attacks. These testing datasets provide useful benchmarks in the realm of deepfake detection for further research. Moreover, results demonstrate that (1) the deepfake detection accuracy can be significantly and consistently improved (from 71.06% to 92.11%, in average accuracy over unseen domains) using pretrained vision-language models with prompt tuning; (2) our superior performance is at less cost of training data and trainable parameters, resulting in an effective and efficient solution for deepfake detection. Code and models can be found at https://github.com/nctu-eva-lab/AntifakePrompt.

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Cited by 14 Pith papers

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

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    Adversarial training between a diffusion image editor and a reasoning MLLM produces an AI-image detector that improves across rounds on three external benchmarks.

  2. LHSDet: High-Resolution AI-Generated Image Detection via Visual Question Answering

    cs.CV 2026-08 conditional novelty 6.0 of 10

    LHSDet detects AI-generated images by fusing low-level texture features, SigLIP2 global features, and BLIP-2 captions in a visual question answering framework, reaching 96% average cross-model accuracy.

  3. GlobalForge: Towards Robust AI-Generated Image Detection

    cs.CV 2026-07 conditional novelty 6.0 of 10

    GlobalForge improves AI-generated image detection under real-world degradation by suppressing local shortcuts and enforcing long-range structural reasoning, outperforming prior state-of-the-art by 5.89% average balanc...

  4. AIGI-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models

    cs.CV 2025-07 conditional novelty 6.0 of 10

    AIGI-Holmes combines visual expert pretraining, SFT on explanation data, and direct preference optimization to deliver human-verifiable explanations and top detection accuracy on unseen AI generators.

  5. Interpretable and Reliable Detection of AI-Generated Images via Grounded Reasoning in MLLMs

    cs.CV 2025-06 conditional novelty 6.0 of 10

    Fine-tuning Qwen-2.5-VL on the new FakeXplained dataset of 8,772 AI-generated images with box-and-caption artifact annotations yields an explainable detector with 98.1% accuracy and 37.8% IoU.

  6. AuthGuard: Generalizable Deepfake Detection via Language Guidance

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    AuthGuard trains a deepfake vision encoder with MLLM-generated text descriptions plus uncertainty-weighted contrastive learning, improving cross-dataset deepfake detection and adding interpretable LLM reasoning.

  7. Knowledge-Guided Prompt Learning for Deepfake Facial Image Detection

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A frozen CLIP model with prompts guided by GPT-4 concepts plus test-time prompt tuning achieves 0.911 average AUC and 0.774 overall accuracy on the DeepFakeFaceForensics benchmark.

  8. A Bias-Free Training Paradigm for More General AI-generated Image Detection

    cs.CV 2024-12 conditional novelty 6.0 of 10

    B-Free trains an AI-image detector on self-conditioned Stable Diffusion 2.1 reconstructions of COCO photos plus inpainting-based augmentation, improving generalization to unseen generators and calibration.

  9. Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations

    cs.MM 2025-06 conditional novelty 5.0 of 10

    MultiFakeVerse provides 845,286 person-centric images edited through VLM-generated instructions; state-of-the-art deepfake detectors and human observers misclassify a large fraction of them.

  10. DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images

    cs.CV 2025-04 conditional novelty 5.0 of 10

    A LoRA-tuned CLIP-ViT with deep-shallow feature fusion and triplet loss detects images from unseen GANs and diffusion models at 89% average accuracy when trained only on four ProGAN classes.

  11. Simple Domain Generalization for Strong Pixel-Level Image Tampering Detection in Modern VLMs

    cs.CV 2026-07 reject novelty 4.0 of 10

    A simple domain-generalization training recipe (balanced real/tampered batches, late injection of a companion VLM domain, low learning rate) yields large pixel-level tampering-localization gains on out-of-distribution...

  12. Visual Language Models as Zero-Shot Deepfake Detectors

    cs.CV 2025-07 conditional novelty 4.0 of 10

    Zero-shot VLMs scored by normalized yes/no token probabilities beat most trained deepfake detectors on a new SimSwap dataset, and a lightly fine-tuned InstructBLIP is near-perfect on DFDC-P.

  13. LLMs Are Not Yet Ready for Deepfake Image Detection

    cs.CV 2025-06 conditional novelty 4.0 of 10

    On a 100-image benchmark of real and fake faces, ChatGPT, Claude, Gemini, and Grok all fell short of dependable zero-shot deepfake detection, with accuracy varying by category and model.

  14. 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.

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