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    <abstract>The rising sophistication of digital surveillance poses hurdles for concealing sensitive data within innocuous communication channels. Conventional image steganography relies on detectable pixel-level perturbations. In this paper, we introduce a novel steganography framework that fundamentally reorients the steganographic containers from the visual domain to the linguistic domain. To seamlessly bridge the gap from raw pixels to discriminative logits, we leverage the reversible latent space of discrete diffusion models to compress high-resolution secret images into lightweight binary payloads. The semantic stability of textual data ensures the integrity of the hidden payload across diverse platforms. Extensive evaluations confirm that this cross-modal approach establishes a superior equilibrium between embedding capacity and statistical undetectability in comparison to existing paradigms.</abstract>
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%0 Conference Proceedings
%T Steganography Beyond Pixels: Reimagining Image Steganography as Cross-Modal Linguistic Communication
%A Lijing, Ren
%A Zhang, Denghui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lijing-zhang-2026-steganography
%X The rising sophistication of digital surveillance poses hurdles for concealing sensitive data within innocuous communication channels. Conventional image steganography relies on detectable pixel-level perturbations. In this paper, we introduce a novel steganography framework that fundamentally reorients the steganographic containers from the visual domain to the linguistic domain. To seamlessly bridge the gap from raw pixels to discriminative logits, we leverage the reversible latent space of discrete diffusion models to compress high-resolution secret images into lightweight binary payloads. The semantic stability of textual data ensures the integrity of the hidden payload across diverse platforms. Extensive evaluations confirm that this cross-modal approach establishes a superior equilibrium between embedding capacity and statistical undetectability in comparison to existing paradigms.
%U https://aclanthology.org/2026.acl-long.1030/
%P 22490-22501

Markdown (Informal)

[Steganography Beyond Pixels: Reimagining Image Steganography as Cross-Modal Linguistic Communication](https://aclanthology.org/2026.acl-long.1030/) (Lijing & Zhang, ACL 2026)

• Steganography Beyond Pixels: Reimagining Image Steganography as Cross-Modal Linguistic Communication (Lijing & Zhang, ACL 2026)

ACL

• Ren Lijing and Denghui Zhang. 2026. Steganography Beyond Pixels: Reimagining Image Steganography as Cross-Modal Linguistic Communication. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 22490–22501, San Diego, California, United States. Association for Computational Linguistics.