IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and
  Video AIGC Detection
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.
683e5e514c5b9f381d47c546
Wayne Zhang
683e5e514c5b9f381d47c547
Changjiang Jiang
claimed_verified
2025-06-03T08:46:29.222Z
652fc2605615e57807e3db19
https://cdn-avatars.huggingface.co/v1/production/uploads/652fc2605615e57807e3db19/kbRcpR0YFQnU3IlziqCHf.png
Changjiang Jiang
user
arnodjiang
683e5e514c5b9f381d47c548
Zhonghao Zhang
683e5e514c5b9f381d47c549
Chenyang Si
claimed_verified
2025-06-10T09:29:27.754Z
635f8ed47c05eb9f59963d3a
https://cdn-avatars.huggingface.co/v1/production/uploads/635f8ed47c05eb9f59963d3a/uQf4p9N9pSaFy87Wg9v4k.jpeg
ChenyangSi
user
ChenyangSi
683e5e514c5b9f381d47c54a
Fengchang Yu
683e5e514c5b9f381d47c54b
Wei Peng
IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and
  Video AIGC Detection
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.
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.
diffusion-based architectures IVY-FAKE Ivy Explainable Detector (IVY-XDETECTOR) unified AIGC detection vision-language model