Recent advances in deep generative models have led to the development of methods capable of synthesizing high-quality, realistic images. These models pose threats to society due to their potential misuse. Prior research attempted to mitigate these threats by detecting generated images, but the varying traces left by different generative models make it challenging to create a universal detector capable of generalizing to new, unseen generative models. In this paper, we propose to inject a universal adversarial signature into an arbitrary pre-trained generative model, in order to make its generated contents more detectable and traceable. First, the imperceptible optimal signature for each image can be found by a signature injector through adversarial training. Subsequently, the signature can be incorporated into an arbitrary generator by fine-tuning it with the images processed by the signature injector. In this way, the detector corresponding to the signature can be reused for any fine-tuned generator for tracking the generator identity. The proposed method is validated on the FFHQ and ImageNet datasets with various state-of-the-art generative models, consistently showing a promising detection rate. Code will be made publicly available at \url{https://github.com/zengxianyu/genwm}.

翻译：深度生成模型的最新进展催生了能够合成高质量逼真图像的方法。这些模型因其潜在滥用风险对社会构成威胁。先前研究尝试通过检测生成图像来缓解这些威胁，但不同生成模型留下的差异痕迹使得创建能够泛化到未知新型生成模型的通用检测器极具挑战性。本文提出向任意预训练生成模型中注入通用对抗签名，以增强其生成内容的可检测性与可追溯性。首先，通过对抗训练方式，签名注入器可为每张图像找到不可察觉的最优签名。随后，通过用经签名注入器处理的图像对任意生成器进行微调，可将该签名嵌入其中。由此，与签名对应的检测器可复用于任何微调生成器，以追踪生成器身份。所提方法在FFHQ和ImageNet数据集上，基于多种先进生成模型验证，均展现出可靠的检测率。代码将于\url{https://github.com/zengxianyu/genwm}开源。