The vast amounts of digital content captured from the real world or AI-generated media necessitate methods for copyright protection, traceability, or data provenance verification. Digital watermarking serves as a crucial approach to address these challenges. Its evolution spans three generations: handcrafted, autoencoder-based, and foundation model based methods. %Its evolution spans three generations: handcrafted methods, autoencoder-based schemes, and methods based on foundation models. While the robustness of these systems is well-documented, the security against adversarial attacks remains underexplored. This paper evaluates the security of foundation models' latent space digital watermarking systems that utilize adversarial embedding techniques. A series of experiments investigate the security dimensions under copy and removal attacks, providing empirical insights into these systems' vulnerabilities. All experimental codes and results are available at https://github.com/vkinakh/ssl-watermarking-attacks}{repository

本研究解决了数字水印系统在对抗攻击下的安全性问题，填补了这一领域的空白。文章采用了新颖的对抗嵌入技术，对基于基础模型的数字水印方法的安全性进行了评估，并在复制和去除攻击下进行了系列实验，揭示了系统的脆弱性。研究结果为提升水印技术的安全性提供了实证依据，具有重要的应用价值。

基于机器学习的水印安全性评估：复制与去除攻击