IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection

• URL: http://arxiv.org/abs/2506.00979v1
• Date: Sun, 01 Jun 2025 12:20:22 GMT
• Title: IVY-FAKE: A Unified Explainable Framework and Benchmark for Image and Video AIGC Detection
• Authors: Wayne Zhang, Changjiang Jiang, Zhonghao Zhang, Chenyang Si, Fengchang Yu, Wei Peng,
• Abstract summary: We introduce IVY-FAKE, a novel, unified, and large-scale dataset for explainable multimodal AIGC detection.<n>We propose Ivy Explainable Detector (IVY-XDETECTOR), a unified AIGC detection and explainable architecture.<n>Our unified vision-language model achieves state-of-the-art performance across multiple image and video detection benchmarks.
• Score: 24.67072921674199
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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