Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks

• URL: http://arxiv.org/abs/2409.18211v2
• Date: Fri, 4 Oct 2024 18:03:51 GMT
• Title: Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks
• Authors: Vitaliy Kinakh, Brian Pulfer, Yury Belousov, Pierre Fernandez, Teddy Furon, Slava Voloshynovskiy,
• Abstract summary: Digital watermarking serves as a crucial approach to address these challenges. This paper evaluates the security of foundation models' latent space digital watermarking systems that utilize adversarial embedding techniques.
• Score: 12.898088696134705
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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 .

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