SimLBR：通过学习检测真实图像来检测伪造图像
SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images
提出在潜空间对真实图像做混合正则化来学习紧致的真实分布边界，将伪造类别视为汇点类，这种思路可迁移到其他需要检测分布外样本的视觉任务（如异常检测、开放集识别）。
原文依据 we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR)
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论生成模型的快速发展使得AI生成图像的检测成为研究和社会面临的关键挑战。近期工作表明，大多数最优的伪造图像检测方法在其训练数据上过拟合，在具有强分布偏移的精选困难测试集上评估时会灾难性地失败。在本工作中，我们认为更合理的做法是学习围绕真实图像分布的紧致决策边界，并将伪造类别视为汇点类。为此，我们提出了SimLBR，一个简单高效的伪造图像检测框架，采用潜变量混合正则化（LBR）。我们的方法显著提升了跨生成器泛化能力，在具有挑战性的Chameleon基准上实现了高达+24.85%的准确率和+69.62%的召回率。SimLBR还具有很高的效率，训练速度比现有方法快几个数量级。此外，我们强调伪造图像检测中面向可靠性的评估需求，引入了风险调整指标和最坏情况估计，以更好地评估模型鲁棒性。所有代码和模型可在 https://github.com/mvrl/SimLBR 获取。
英文原文摘要 1203 字符
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 with 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 the code and models are availabe at: \href https://github.com/mvrl/SimLBR https://github.com/mvrl/SimLBR .