title: IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and
    Video AIGC Detection
summary: The rapid advancement of Artificial Intelligence Generated Content (AIGC) in
  visual domains has resulted in highly realistic synthetic images and videos,
  driven by sophisticated generative frameworks such as diffusion-based
  architectures. While these breakthroughs open substantial opportunities, they
  simultaneously raise critical concerns about content authenticity and
  integrity. Many current AIGC detection methods operate as black-box binary
  classifiers, which offer limited interpretability, and no approach supports
  detecting both images and videos in a unified framework. This dual limitation
  compromises model transparency, reduces trustworthiness, and hinders practical
  deployment. To address these challenges, we introduce IVY-FAKE , a novel,
  unified, and large-scale dataset specifically designed for explainable
  multimodal AIGC detection. Unlike prior benchmarks, which suffer from
  fragmented modality coverage and sparse annotations, IVY-FAKE contains over
  150,000 richly annotated training samples (images and videos) and 18,700
  evaluation examples, each accompanied by detailed natural-language reasoning
  beyond simple binary labels. Building on this, we propose Ivy Explainable
  Detector (IVY-XDETECTOR), a unified AIGC detection and explainable architecture
  that jointly performs explainable detection for both image and video content.
  Our unified vision-language model achieves state-of-the-art performance across
  multiple image and video detection benchmarks, highlighting the significant
  advancements enabled by our dataset and modeling framework. Our data is
  publicly available at https://huggingface.co/datasets/AI-Safeguard/Ivy-Fake.
paper_authors._id: 683e5e514c5b9f381d47c546
paper_authors.name: Wayne Zhang
paper_authors._id: 683e5e514c5b9f381d47c547
paper_authors.name: Changjiang Jiang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-03T08:46:29.222Z
paper_authors._id: 652fc2605615e57807e3db19
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/652fc2605615e57807e3db19/kbRcpR0YFQnU3IlziqCHf.png
paper_authors.fullname: Changjiang Jiang
paper_authors.type: user
paper_authors.user: arnodjiang
paper_authors._id: 683e5e514c5b9f381d47c548
paper_authors.name: Zhonghao Zhang
paper_authors._id: 683e5e514c5b9f381d47c549
paper_authors.name: Chenyang Si
paper_authors._id: 683e5e514c5b9f381d47c54a
paper_authors.name: Fengchang Yu
paper_authors._id: 683e5e514c5b9f381d47c54b
paper_authors.name: Wei Peng
paper_title: IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and
    Video AIGC Detection
paper_summary: The rapid advancement of Artificial Intelligence Generated Content (AIGC) in
  visual domains has resulted in highly realistic synthetic images and videos,
  driven by sophisticated generative frameworks such as diffusion-based
  architectures. While these breakthroughs open substantial opportunities, they
  simultaneously raise critical concerns about content authenticity and
  integrity. Many current AIGC detection methods operate as black-box binary
  classifiers, which offer limited interpretability, and no approach supports
  detecting both images and videos in a unified framework. This dual limitation
  compromises model transparency, reduces trustworthiness, and hinders practical
  deployment. To address these challenges, we introduce IVY-FAKE , a novel,
  unified, and large-scale dataset specifically designed for explainable
  multimodal AIGC detection. Unlike prior benchmarks, which suffer from
  fragmented modality coverage and sparse annotations, IVY-FAKE contains over
  150,000 richly annotated training samples (images and videos) and 18,700
  evaluation examples, each accompanied by detailed natural-language reasoning
  beyond simple binary labels. Building on this, we propose Ivy Explainable
  Detector (IVY-XDETECTOR), a unified AIGC detection and explainable architecture
  that jointly performs explainable detection for both image and video content.
  Our unified vision-language model achieves state-of-the-art performance across
  multiple image and video detection benchmarks, highlighting the significant
  advancements enabled by our dataset and modeling framework. Our data is
  publicly available at https://huggingface.co/datasets/AI-Safeguard/Ivy-Fake.
paper_ai_summary: IVY-FAKE dataset and Ivy Explainable Detector (IVY-XDETECTOR) architecture address the limitations of current AIGC detection by providing a unified, explainable framework for images and videos.
paper_ai_keywords: diffusion-based architectures IVY-FAKE Ivy Explainable Detector (IVY-XDETECTOR) unified AIGC detection vision-language model