Diffusion-Generated Image Watermarking: A Two-Axis Taxonomy and Three Protocol-Bounded Case Studies

Key points

• This work organizes methods along two axes: insertion mechanism and primary signal-bearing representation, and formalizes a representative $zT$-Fourier pipeline for verification and identification.1
• The second revisits persistence under seed-linked and seed-independent editing and formulates a scoped Semantic Imprinting Hypothesis without claiming a localized carrier or causal mechanism.1
• Finally, we separate four content-level attack families from model/pipeline adaptation, propose corresponding evaluation protocols and testable conjectures for parameter-tuning threats, and identify additional temporal extensions for video.1
• These analyses do not establish a universal ranking; instead, they provide a framework for matched, protocol-aware comparisons of watermarking systems for diffusion-generated images.1

Sources (1)

• [1]Diffusion-Generated Image Watermarking: A Two-Axis Taxonomy and Three Protocol-Bounded Case Studies
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 09:42 AM

  Watermarking diffusion-generated images requires balancing provenance signals with image quality, robustness, and computational cost.

  This work organizes methods along two axes: insertion mechanism and primary signal-bearing representation, and formalizes a representative $z_T$-Fourier pipeline for verification and identification.

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