← 研踪 PaperTrailACL Anthology · ACL 2026:acl:2026.acl-long.1030 · 记录 #1
Steganography Beyond Pixels: Reimagining Image Steganography as Cross-Modal Linguistic Communication
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:56
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
Steganography Beyond Pixels: Reimagining Image Steganography as Cross-Modal Linguistic Communication
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.
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