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DiffusionFace: Towards a Comprehensive Dataset for Diffusion-Based Face Forgery Analysis

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arxiv 2403.18471 v1 pith:6PGXC4FU submitted 2024-03-27 cs.CV

Zhongxi Chen , Ke Sun , Ziyin Zhou , Xianming Lin , Xiaoshuai Sun , Liujuan Cao , Rongrong Ji This is my paper
classification cs.CV
keywords facialforgerydatasetdiffusion-baseddiffusionfacefaceimageimages
verification ladder T0 review T1 audit T2 compute T3 formal
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The rapid progress in deep learning has given rise to hyper-realistic facial forgery methods, leading to concerns related to misinformation and security risks. Existing face forgery datasets have limitations in generating high-quality facial images and addressing the challenges posed by evolving generative techniques. To combat this, we present DiffusionFace, the first diffusion-based face forgery dataset, covering various forgery categories, including unconditional and Text Guide facial image generation, Img2Img, Inpaint, and Diffusion-based facial exchange algorithms. Our DiffusionFace dataset stands out with its extensive collection of 11 diffusion models and the high-quality of the generated images, providing essential metadata and a real-world internet-sourced forgery facial image dataset for evaluation. Additionally, we provide an in-depth analysis of the data and introduce practical evaluation protocols to rigorously assess discriminative models' effectiveness in detecting counterfeit facial images, aiming to enhance security in facial image authentication processes. The dataset is available for download at \url{https://github.com/Rapisurazurite/DiffFace}.

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Cited by 6 Pith papers

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

  1. When Generative Replay Meets Evolving Deepfakes: Dual Confusion-Aware Regularization for Incremental Face Forgery Detection

    cs.CV 2025-11 conditional novelty 6.0 of 10

    Adaptively weighting direct supervision versus a relative-separation loss makes generative replay work for incremental deepfake detection.

  2. 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.

  3. 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.

  4. SEED: A Benchmark Dataset for Sequential Facial Attribute Editing with Diffusion Models

    cs.CV 2025-05 reject novelty 6.0 of 10

    SEED is a 91,526-image benchmark of diffusion-generated sequential facial edits with sequence, mask, and prompt annotations, and FAITH adds DWT high-frequency cues to a transformer for edit-sequence detection.

  5. InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection

    cs.CV 2026-07 conditional novelty 5.0 of 10

    InfoDense replays only density-ranked, forgery-decisive face fragments rather than full images, cutting memory use and improving incremental deepfake detection.

  6. Bi-Level Optimization for Self-Supervised AI-Generated Face Detection

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Bi-level reweighting of EXIF and manipulation pretext tasks yields a photo-only encoder that beats prior detectors on cross-generator AI-face detection.

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