通过学习真实图像边界来检测假图，提升泛化能力。
SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
- 构建真实图像的紧致决策边界，将假图视为异常类
- 在Chameleon基准上准确率提升24.85%，召回率提升69.62%
- 训练速度远超现有方法，适合高可靠性检测场景
生成模型的快速发展使AI生成图像的检测成为研究与社会的重要挑战。现有先进检测方法普遍在训练数据上过拟合，面对带有强分布偏移的精心设计测试集时表现急剧下降。本文主张更合理的方法是围绕真实图像分布学习紧致决策边界，并将假图像视为“汇类”。为此，我们提出SimLBR框架，采用潜在空间混合正则化（LBR）实现高效检测。该方法显著提升跨生成器泛化性能，在挑战性Chameleon基准上准确率最高提升24.85%，召回率最高提升69.62%。SimLBR训练效率极高，较现有方法快多个数量级。此外，我们强调应以可靠性为导向进行评估，引入风险调整指标与最坏情况估计，更准确衡量模型鲁棒性。所有代码与模型将开源至HuggingFace与GitHub。
原文摘要 · Abstract (English)
The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection using Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85\% accuracy and +69.62\% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All code and models will be released on HuggingFace and GitHub.
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