Raster Domain Text Steganography: A Unified Framework for Multimodal Secure Embedding

TL;DR

This paper presents a unified raster domain steganography framework that embeds multimodal data into text glyphs by minimally perturbing pixel ink counts, enabling imperceptible, stable, and decodable covert communication directly in rendered text images.

Contribution

The paper introduces the Glyph Perturbation Cardinality (GPC) framework, a novel method for embedding diverse data types into rasterized text glyphs through pixel perturbation, operating post-rendering.

Findings

Effective embedding of text, image, audio, and video data into text glyphs.

Minimal perturbations remain visually imperceptible while enabling reliable decoding.

Framework is computationally lightweight and compatible with standard text rendering pipelines.

Abstract

This work introduces a unified raster domain steganographic framework, termed as the Glyph Perturbation Cardinality (GPC) framework, capable of embedding heterogeneous data such as text, images, audio, and video directly into the pixel space of rendered textual glyphs. Unlike linguistic or structural text based steganography, the proposed method operates exclusively after font rasterization, modifying only the bitmap produced by a deterministic text rendering pipeline. Each glyph functions as a covert encoding unit, where a payload value is expressed through the cardinality of minimally perturbed interior ink pixels. These minimal intensity increments remain visually imperceptible while forming a stable and decodable signal. The framework is demonstrated for text to text embedding and generalized to multimodal inputs by normalizing image intensities, audio derived scalar features, and video frame values into bounded integer sequences distributed across glyphs. Decoding is achieved by re-rasterizing the cover text, subtracting canonical glyph rasters, and recovering payload values via pixel count analysis. The approach is computationally lightweight, and grounded in deterministic raster behavior, enabling ordinary text to serve as a visually covert medium for multimodal data embedding.