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Attack-Resilient Multi-Bit Image Watermarking Framework via Latent Coordinate Sign Clamping — arXiv:2610.40204

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.40204
License (as archived): CC0-1.0
Archived: 2026-11-01T02:21:20.971Z
SHA-256 of the archived text: 67464f31256285a4663688ea67098aeb673bbc82d40effc5bd2d4fa3e69b983a
Stable URL of this page: https://metraipolis.com/documento/dc_da789f0feedb

How to cite

Attack-Resilient Multi-Bit Image Watermarking Framework via Latent Coordinate Sign Clamping — arXiv:2610.40204 — arXiv (en), https://arxiv.org/abs/2610.40204 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_da789f0feedb (Metraipolis archive dc_da789f0feedb, sha256 67464f31256285a4663688ea67098aeb673bbc82d40effc5bd2d4fa3e69b983a)

BibTeX

@misc{dcda789f0feedb,
  title = {Attack-Resilient Multi-Bit Image Watermarking Framework via Latent Coordinate Sign Clamping — arXiv:2610.40204},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.40204},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_da789f0feedb; sha256 67464f31256285a4663688ea67098aeb673bbc82d40effc5bd2d4fa3e69b983a},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.40204. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Watermarking is essential for image copyright protection and attribution, yet existing methods suffer from fundamental trade-offs among post-hoc applicability to pre-existing images, multi-bit capacity, training-free deployment, and adversarial resilience. To address these limitations, we propose a training-free watermarking framework that exploits the overcompleteness of diffusion latent spaces to embed multi-bit payloads into arbitrary images while preserving high visual quality. Additionally, we develop a three-layer decoding pipeline that combines magnitude-weighted majority voting, BCH, and Chase decoding to maximize message-extraction accuracy in the presence of channel noise. To counter active adversaries, our framework integrates a steganalysis-resistant noise camouflage layer and a dual-layer forgery defense that combines perceptual hashing with a Reed-Solomon fuzzy commitment scheme over CLIP features. Empirical evaluations demonstrate that our method applies to both natural and AI-generated images with a scalable $32\text{--}128$-bit capacity. Furthermore, it effectively mitigates advanced removal vectors ($94.6\%$ decode rate against CtrlRegen+ regeneration attacks) and secures robust forgery detection ($98.8\%$ accuracy via joint CLIP and RS fuzzy commitment mechanism).

Tema degli esploratori: fiducia digitale e crittografia (elenco arXiv cs.CR, nuovi lavori del giorno).
Categorie arXiv: Cryptography and Security (cs.CR).
Pagina del lavoro: https://arxiv.org/abs/2610.40204 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-01T02:12:13.405Z dalla pagina https://arxiv.org/list/cs.CR/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).