The illegal use of facial forgery models, such as Generative Adversarial Networks (GAN) synthesized contents, has been on the rise, thereby posing great threats to personal reputation and national security. To mitigate these threats, recent studies have proposed the use of adversarial watermarks as countermeasures against GAN, effectively disrupting their outputs. However, the majority of these adversarial watermarks exhibit very limited defense ranges, providing defense against only a single GAN forgery model. Although some universal adversarial watermarks have demonstrated impressive results, they lack the defense scalability as a new-emerging forgery model appears. To address the tough issue, we propose a scalable approach even when the original forgery models are unknown. Specifically, a watermark expansion scheme, which mainly involves inheriting, defense and constraint steps, is introduced. On the one hand, the proposed method can effectively inherit the defense range of the prior well-trained adversarial watermark; on the other hand, it can defend against a new forgery model. Extensive experimental results validate the efficacy of the proposed method, exhibiting superior performance and reduced computational time compared to the state-of-the-arts.

中文翻译：

---

可扩展的通用对抗水印防御面部伪造


非法使用面部伪造模型，如生成对抗网络 （GAN） 合成内容，呈上升趋势，对个人声誉和国家安全构成巨大威胁。为了减轻这些威胁，最近的研究建议使用对抗性水印作为对抗 GAN 的对策，从而有效地破坏其输出。然而，这些对抗性水印中的大多数表现出非常有限的防御范围，只能抵御单一的 GAN 伪造模型。尽管一些通用的对抗性水印已经显示出令人印象深刻的结果，但它们缺乏防御可扩展性，因为出现了一种新兴的伪造模型。为了解决这个棘手的问题，我们提出了一种可扩展的方法，即使原始伪造模型未知。具体来说，介绍了一种水印扩展方案，主要涉及继承、防御和约束步骤。一方面，所提方法能够有效继承先验训练良好的对抗水印的防御范围;另一方面，它可以抵御新的伪造模型。广泛的实验结果验证了所提出的方法的有效性，与最先进的方法相比，表现出卓越的性能和更少的计算时间。