Investigating Deep Watermark Security: An Adversarial Transferability Perspective

arXiv:2402.16397v1 Announce Type: new
Abstract: The rise of generative neural networks has triggered an increased demand for intellectual property (IP) protection in generated content. Deep watermarking techniques, recognized for their flexibility in IP protection, have garnered significant attention. However, the surge in adversarial transferable attacks poses unprecedented challenges to the security of deep watermarking techniques-an area currently lacking systematic investigation. This study fills this gap by introducing two effective transferable attackers to assess the vulnerability of deep watermarks against erasure …

adversarial arxiv attacks attention challenges cs.ai cs.cr demand flexibility generated generative intellectual property ip protection networks neural networks perspective property protection security techniques unprecedented watermarking