EIB-Net: エントロピー誘導情報ボトルネックによる汎化可能なAI生成画像検出
EIB-Net: Entropy-Guided Information Bottleneck for Generalizable AI-Generated Image Detection
生成画像は低テクスチャ領域に合成痕跡が現れる点に着目し、画像エントロピーで情報量の多いパッチを選び、変分情報ボトルネックで汎化性の高い特徴を学習する検出手法を提案。少ない学習データで高精度・クロス生成器汎化を実現。
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著者: Zhida Zhang, Xinlei Ma, Jie Cao
分類: cs.CV, cs.AI
原文アブストラクト
The proliferation of photorealistic AI-generated images demands robust detection methods that generalize across diverse generative models. While existing approaches target manipulation-based forgeries with local artifacts, generation-based images (e.g., from diffusion models) lack such traces, posing a fundamental challenge. We observe that generative models prioritize global semantics at the expense of local texture fidelity, making low-texture regions key indicators of synthetic origin. To exploit this, we propose EIB-Net, an Entropy-guided Information Bottleneck Network. EIB-Net introduces a novel Image Entropy (IE) metric to automatically select the most informative (lowest-entropy) patch, then processes it with a Variational Information Bottleneck (VIB) to learn compact, generalizable features. Extensive experiments on DIFF, DiffusionForensics, and GenImage benchmarks demonstrate state-of-the-art performance: EIB-Net achieves 85.7\% accuracy using only 2\% of training data, outperforming full-image baselines by over 15\%, and maintains robust cross-generator generalization (83.5\% average accuracy on GenImage). Furthermore, our entropy-guided patch selection (EGPL) consistently enhances diverse backbones (CNNs and Transformers), proving its practical value for data-efficient detection.