As generative AI progresses rapidly, new synthetic image generators continue to emerge at a swift pace. Traditional detection methods face two main challenges in adapting to these generators: the forensic traces of synthetic images from new techniques can vastly differ from those learned during training, and access to data for these new generators is often limited. To address these issues, we introduce the Ensemble of Expert Embedders (E3), a novel continual learning framework for updating synthetic image detectors. E3 enables the accurate detection of images from newly emerged generators using minimal training data. Our approach does this by first employing transfer learning to develop a suite of expert embedders, each specializing in the forensic traces of a specific generator. Then, all embeddings are jointly analyzed by an Expert Knowledge Fusion Network to produce accurate and reliable detection decisions. Our experiments demonstrate that E3 outperforms existing continual learning methods, including those developed specifically for synthetic image detection.

翻译：随着生成式人工智能的快速发展，新型合成图像生成器持续以迅猛速度涌现。传统检测方法在适应这些生成器时面临两大挑战：新技术生成的合成图像所遗留的取证痕迹与训练阶段学习的特征可能存在显著差异，且获取这些新型生成器的训练数据通常非常有限。为解决上述问题，我们提出了专家嵌入器集成方法（E3），这是一种用于更新合成图像检测器的持续学习框架。E3能够通过使用极少量训练数据精确检测来自新型生成器的图像。该方法首先通过迁移学习开发一系列专家嵌入器，每个嵌入器专注于特定生成器的取证痕迹。随后，所有嵌入结果由专家知识融合网络联合分析，产生准确且可靠的检测决策。实验表明，E3在性能上超越了现有持续学习方法，包括专门为合成图像检测设计的方案。