With the rapid proliferation of generative models, such as diffusion models, digital watermarking has emerged as a crucial solution for identifying AI-generated images. Modern post-hoc watermarking schemes use neural networks to achieve an extremely low false-alarm rate while remaining robust to common image transformations. However, there is a lack of comparison between these modern methods and classic ones, particularly in real-world scenarios where robustness and security take precedence over achieving an extremely low false-alarm probability. In this paper, we propose a fair comparison of robustness and security between modern and classic post-hoc watermarking across various types of classic augmentations and recent sophisticated attacks. Our experiments show that, in a realistic scenario, classic watermarking outperforms modern techniques in terms of security while maintaining robustness.

翻译：随着扩散模型等生成模型的快速普及，数字水印已成为识别AI生成图像的关键技术。现代事后水印方案通过神经网络实现了极低的误报率，同时保持对常见图像变换的鲁棒性。然而，当鲁棒性与安全性优先于极低误报概率的实际场景中，这些现代方法与经典方法之间缺乏系统性对比。本文提出对现代与经典事后水印方法进行公平比较，涵盖多种经典数据增强手段及新型复杂攻击场景。实验表明，在实际场景中，经典水印方法在保持鲁棒性的同时，其安全性优于现代技术。