Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks
<|reference_start|>Evaluation of Security of ML-based Watermarking: Copy and Removal Attacks: 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 .<|reference_end|>
@article{kinakh2024evaluation,
    title={Evaluation of Security of ML-based Watermarking: Copy and Removal
  Attacks},
    author={Vitaliy Kinakh, Brian Pulfer, Yury Belousov, Pierre Fernandez, Teddy
  Furon, Slava Voloshynovskiy},
    journal={arXiv preprint arXiv:2409.18211},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18211},
    primaryClass={cs.CV}
}