Securing Deep Generative Models with Universal Adversarial Signature
Yu Zeng Mo Zhou Yuan Xue Vishal M. Patel
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}.