IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and
  Video AIGC Detection
Authors: name: Wayne Zhang; name: Changjiang Jiang; name: Zhonghao Zhang; name: Chenyang Si; name: Fengchang Yu; name: Wei Peng
Published: 2025-06-01 12:20:22

Abstract: 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.