A curated collection of the latest research and resources on AI-Generated Image and Video Detection. This repository encompasses datasets, benchmarks, research papers, and practical detection tools.

🚀🚀🚀Contributions are welcome! If you find any missing papers, datasets, or tools, feel free to open an issue or submit a pull request.

• Hot Events
• Benchmarks & Datasets
• Research Papers
  • MLLM-Based
  • Classification-Based
• Competitions
• Practical Detection Tools
• About Our Team

• 因为GPT-image-2，整个互联网都变成了巨大的黑暗森林

• ChatGPT 图像 2.0 现已上线! 开启图像生成的新纪元

• 谷歌Gemini生图的暗水印，被印度小哥破解了，成功率90%，Github揽星3k+

• 炸裂！技术力究极恐怖！米哈游蔡浩宇首款AI生成视频大模型LPM1.0曝光！自定义虚拟角色？支持多语言+无限真实+无限时长，实现自由对话、唱歌、表演等效果

• ⭐️ 刚刚，阿里官方认领神秘「欢乐马」(HappyHorse)，来自ATH郑波团队

• 就AI换脸合成、影视素材魔改等，中广联演员委员会发声明

• Is the new video of Netanyahu AI-generated?

• Netanyahu, disappearing ring!

• 为什么内塔尼亚胡没能成功证明自己没死？

• A photo of Iran’s bombed schoolgirl graveyard went viral. Why did AI say it wasn’t real?

• How AI Content Detection is Being Weaponized in the Iran War

• 改变视频行业的AI，快来了(但有点恐怖) - Bilibili 影视飓风

• 勘误致歉：今日发布的库里采访资讯为AI杜撰生成

• 聊聊关于Seedance 2.0的风波 - 数字生命卡兹克

• Trump unveils his vision for 'Gaza Riviera' with AI video featuring belly dancers and luxury yachts

• 电商上演「魔法对轰」：卖家用AI假图骗下单，买家拿AI烂水果骗退款

Modality Legend: [I] Image | [V] Video | [M] Multi-modal

Annotation Type Legend: Au: Authenticity | Ex: Explainability | Lo: Localization

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💡 Note: Papers are sorted by year (descending) within each category.
Modality Legend: [I] Image | [V] Video | [M] Multi-modal

This category focuses on utilizing Multimodal Large Language Models (MLLMs) like GPT-4V, LLaVA, or Qwen-VL to detect AI-generated content. These methods often provide natural language explanations (explainability) alongside binary detection.

This category includes supervised learning approaches that train neural networks (CNNs, ViTs, etc.) specifically to classify authentic vs. AI-generated content. They usually focus on robustness, generalization, and feature extraction.

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• Hive Moderation - Website
• Tencent Zhuque AI Detection Assistant - Website
• AI or Not - Website
• Illuminarty - Website
• Winston AI - Website
• Is it AI? - Website
• 中科睿鉴 (Zhongke Ruijian) - 微信小程序搜索 睿鉴AI

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We are the Content Security Intelligence Team under Ant Group - Machine Intelligence. We are responsible for developing comprehensive content security and risk-mitigation capabilities for the Ant Group ecosystem, bridging the gap between rapidly evolving technologies and the urgent need for digital trust.

In an era where synthetic media is increasingly sophisticated and pervasive, our research serves as a critical line of defense. By advancing AIGC detection technologies, we aim to:

• Safeguard Digital Integrity: We provide essential defense mechanisms to protect the authenticity of visual content and combat the spread of misinformation in the digital space.
• Empower Trust: Our solutions ensure the public can distinguish between genuine and synthetic media, fostering a more transparent and trustworthy digital ecosystem.
• Industrial Application & Impact: We provide robust, scalable aigc detection solutions for Ant Group’s diverse content platforms, including Lingguang, Jingtan, and many others.

We are honored to collaborate with esteemed researchers and scholars in the field of AI and Computer Vision. We deeply value these academic partnerships that drive our innovation:

• Prof. Jun Wan (万军) | CASIA & UCAS
  • Research Interests: Biometrics, Face Anti-spoofing, Gesture Recognition, and Computer Vision.
  • [Homepage]
• Prof. Jianfu Zhang (张健夫) | Shanghai Jiao Tong University
  • Research Interests: Computer Vision, Pattern Recognition, and Image/Video Analysis & Synthesis.
  • [Homepage]
• Prof. Zhuosheng Zhang (张倬胜) | Shanghai Jiao Tong University
  • Research Interests: Natural Language Processing, Large Language Models, and Multi-modal Learning.
  • [Homepage]

• VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning | (Submitted), 2026
  • Highlights: Detected AI-generated videos using perception pretext reinforcement learning to capture temporal inconsistencies.
  • [Paper] [Code]
• Locate-Then-Examine: Grounded Region Reasoning Improves Detection of AI-Generated Images | CVPR'26, 2026
  • Highlights: Improved detection accuracy through a two-stage approach of localizing suspicious regions followed by detailed examination.
  • [Code]
• GAMMA: Generalizable Alignment via Multi-task and Manipulation-Augmented Training for AI-Generated Image Detection | ICASSP'26, 2026
  • Highlights: Enhanced generalization through multi-task learning and manipulation-augmented training strategies.
  • [Paper]
• FakeXplain: AI-Generated Image Detection via Human-Aligned Grounded Reasoning | ICLR'26, 2026
  • Highlights: Detected AI-generated images through human-aligned grounded reasoning, providing interpretable visual evidence.
  • [Paper] [Code]
• Veritas: Generalizable deepfake detection via pattern-aware reasoning | ICLR'26 Oral, 2026
  • Highlights: Achieved generalizable deepfake detection through pattern-aware reasoning, improving robustness across diverse manipulation types.
  • [Paper] [Code]
• Generalizable and Adaptive Continual Learning Framework for AI-generated Image Detection | IEEE TMM, 2025
  • Highlights: Proposed a continual learning framework that adapts to new generative models while mitigating catastrophic forgetting.
  • [Paper]
• Towards explainable fake image detection with multi-modal large language models | ACM MM'25, 2025
  • Highlights: Leveraged multi-modal large language models to provide human-interpretable explanations for fake image detection.
  • [Paper]
• WildFake: A Large-scale Challenging Dataset for AI-Generated Images Detection | AAAI'25 Oral, 2024
  • Highlights: Introduced the largest and most comprehensive AIGC image dataset at the time, providing a challenging benchmark for detection models.
  • [Paper]

• 1st Place Winner | NTIRE 2026 Robust AI-Generated Image Detection in the Wild Challenge
  • Secured the top rank in ROC AUC for delivering superior performance in large-scale, real-world AI-generated image detection.
  • [Challenge Website]
• 1st Place Winner | ICCV 2025 VQualA Challenge - Image Super-Resolution Generated Content Quality Assessment, 2025
  • Achieved top performance in the VQualA 2025 challenge focused on assessing the quality of super-resolution generated content.
  • [Paper 1] [Paper 2]
• 1st Place Winner | CVPR 2024 Face Anti-Spoofing Challenge, 2024
  • Secured first place in the prestigious Face Anti-Spoofing Challenge at CVPR 2024, demonstrating state-of-the-art detection capabilities.
  • [Challenge Website]

• WildFake - A large and comprehensive AIGC image detection dataset.
  • [ModelScope]
• GenVideo - A large and comprehensive AIGC video detection dataset.
  • [ModelScope]
• HydraFake - A large-scale challenging dataset for AI-generated image detection.
  • [ModelScope]
• MintVid - A comprehensive video dataset for AIGC detection research.
  • [ModelScope]

For questions or collaborations, please contact:

• Zijian Yu: [email protected]
• Hao Tan: [email protected]
• Jun Lan: [email protected]

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