Abstract
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.
Abstract (translated)
随着生成型AI快速发展，新的图像生成器也在以快速的速度涌现。传统检测方法在适应这些生成器时面临着两个主要挑战：来自新技术的合成图像的刑事痕迹与训练期间学习到的有所不同，而这些新生成器的数据获取通常受限。为解决这些问题，我们引入了专家嵌入器（E3）这一新颖的持续学习框架，用于更新合成图像检测器。E3使可以使用极少的训练数据准确地检测出新生成的图像。我们的方法通过首先使用迁移学习开发了一系列专家嵌入器，每个专家专门研究特定生成器的刑事痕迹，然后由专家知识融合网络对所有嵌入进行共同分析，产生准确可靠的检测决策。我们的实验证明，E3超越了专门为合成图像检测而设计的现有持续学习方法。
URL
https://arxiv.org/abs/2404.08814
