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WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection

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arxiv 2402.11843 v1 pith:EXSPSQ35 submitted 2024-02-19 cs.CV

Yan Hong , Jianfu Zhang This is my paper · ORCID
classification cs.CV
keywords wildfakeimagesai-generatedmodelsdatasetgenerativeimagedetection
verification ladder T0 review T1 audit T2 compute T3 formal
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The extraordinary ability of generative models enabled the generation of images with such high quality that human beings cannot distinguish Artificial Intelligence (AI) generated images from real-life photographs. The development of generation techniques opened up new opportunities but concurrently introduced potential risks to privacy, authenticity, and security. Therefore, the task of detecting AI-generated imagery is of paramount importance to prevent illegal activities. To assess the generalizability and robustness of AI-generated image detection, we present a large-scale dataset, referred to as WildFake, comprising state-of-the-art generators, diverse object categories, and real-world applications. WildFake dataset has the following advantages: 1) Rich Content with Wild collection: WildFake collects fake images from the open-source community, enriching its diversity with a broad range of image classes and image styles. 2) Hierarchical structure: WildFake contains fake images synthesized by different types of generators from GANs, diffusion models, to other generative models. These key strengths enhance the generalization and robustness of detectors trained on WildFake, thereby demonstrating WildFake's considerable relevance and effectiveness for AI-generated detectors in real-world scenarios. Moreover, our extensive evaluation experiments are tailored to yield profound insights into the capabilities of different levels of generative models, a distinctive advantage afforded by WildFake's unique hierarchical structure.

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Cited by 8 Pith papers

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

  1. TextRich: A Multi-Domain Benchmark for Detecting AI-Generated Text-Rich Images from GPT-Image-2

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    Introduces a multi-domain benchmark for detecting AI-generated text-rich images from GPT-Image-2 and evaluates five detectors showing domain-dependent performance and JPEG sensitivity.

  2. LEGO: LoRA-Enabled Generator-Oriented Framework for Synthetic Image Detection

    cs.CV 2026-05 unverdicted novelty 7.0 of 10

    LEGO uses multiple generator-specific LoRA modules modulated by an MLP and fused with attention to detect synthetic images, achieving better performance than prior methods while using under 10% of the training data.

  3. 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.

  4. ISPCloak: Weaponizing ISP for Optimization-Free Physical Camouflage against Deepfake Detectors

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Injecting simulated camera sensor noise through an invertible ISP pipeline makes AI-generated images evade multiple deepfake detectors while preserving visual quality.

  5. AgentFoX: LLM Agent-Guided Fusion with eXplainability for AI-Generated Image Detection

    cs.CV 2026-03 conditional novelty 6.0 of 10

    An LLM agent guided by Expert and Clustering Profiles fuses heterogeneous AIGI detectors, resolves conflicts, and outputs explainable forensic reports that beat single experts and standard ensembles on high-conflict a...

  6. A Comprehensive Dataset for Human vs. AI Generated Image Detection

    cs.CV 2026-01 conditional novelty 6.0 of 10

    MS COCOAI provides 96,000 caption-aligned real and synthetic images from five generators, with baseline scores of about 0.80 for real-vs-AI detection and 0.45 for generator attribution.

  7. 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.

  8. Fooling the Watchers: Breaking AIGC Detectors via Semantic Prompt Attacks

    cs.CV 2025-05 reject novelty 4.0 of 10

    A grammar-tree and Monte Carlo search method automatically crafts prompts that can make synthetic portraits evade AIGC detectors, but the reported evidence is sparse and partly contradictory.

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