Investigating Deep Watermark Security: An Adversarial Transferability Perspective
CoRR(2024)
摘要
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 and tampering risks.
Specifically, we initially define the concept of local sample density,
utilizing it to deduce theorems on the consistency of model outputs. Upon
discovering that perturbing samples towards high sample density regions (HSDR)
of the target class enhances targeted adversarial transferability, we propose
the Easy Sample Selection (ESS) mechanism and the Easy Sample Matching Attack
(ESMA) method. Additionally, we propose the Bottleneck Enhanced Mixup (BEM)
that integrates information bottleneck theory to reduce the generator's
dependence on irrelevant noise. Experiments show a significant enhancement in
the success rate of targeted transfer attacks for both ESMA and BEM-ESMA
methods. We further conduct a comprehensive evaluation using ESMA and BEM-ESMA
as measurements, considering model architecture and watermark encoding length,
and achieve some impressive findings.
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