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.

翻译：本研究提出了一种统一的栅格域隐写框架，称为字形扰动基数（GPC）框架，该框架能够将文本、图像、音频和视频等异构数据直接嵌入到渲染文本字形的像素空间中。与基于语言或结构文本的隐写方法不同，所提方法仅在字体栅格化后操作，仅修改确定性文本渲染流程生成的位图。每个字形作为一个隐蔽编码单元，其中有效载荷值通过最小扰动的内部墨迹像素的基数来表达。这些最小强度增量在视觉上不可察觉，同时形成稳定且可解码的信号。该框架通过将图像强度、音频导出的标量特征和视频帧值归一化为分布在字形上的有界整数序列，展示了文本到文本的嵌入，并推广至多模态输入。解码通过重新栅格化载体文本、减去规范字形栅格，并通过像素计数分析恢复有效载荷值来实现。该方法计算轻量，且基于确定性栅格行为，使普通文本能够作为多模态数据嵌入的视觉隐蔽媒介。