AI & ML
Watermarking Without Standards Is Not AI Governance
arXiv cs.CR0 views
arXiv:2505.23814v3 Announce Type: replace Abstract: Watermarking has emerged as a leading technical proposal for attributing generative AI content and is increasingly cited in global governance frameworks. This position paper argues that current implementations risk serving as symbolic compliance rather than delivering effective oversight. We identify a growing gap between regulatory expectations and the technical limitations of existing watermarking schemes. Through analysis of policy proposals and industry practices, we show how incentive structures disincentivize robust, auditable deployments. To realign watermarking with governance goals, we propose a three-layer framework encompassing technical standards, audit infrastructure, and enforcement mechanisms. Without enforceable requirements and independent verification, watermarking will remain inadequate for accountability and ultimately undermine broader efforts in AI safety and regulation.
Source:https://arxiv.org/abs/2505.23814PreviousfaulTPM: Exposing AMD fTPMs' Deepest SecretsNextCryptanalysis of Isogeny-Based Quantum Money with Rational Points
Related posts
Information Loss Is All You Need: $TC^0$ Pseudorandomness from Worst-Case Lattices
0arXiv cs.CR
Backdoor Purification for LoRA-Tuned LLMs via Null-Space Projection
0arXiv cs.CR
Backdoor Containment via Expert Quarantine and Shutdown in LLMs
0arXiv cs.CR
Persona-Model Collapse in Emergent Misalignment
0arXiv cs.CR
Analysis of LLM Vulnerability to GPU Soft Errors: An Instruction-Level Fault Injection Study
0arXiv cs.CR
When appearance and motion disagree: evaluating class-motion consistency against disguised uncrewed surface vessels
0arXiv cs.CR