Embedding Textual Information in Images Using Quinary Pixel Combinations

This paper presents a novel technique for embedding textual data into images using quinary combinations of pixel intensities in RGB space. Existing methods predominantly rely on least and most significant bit (LSB & MSB) manipulation, Pixel Value Differencing (PVD), spatial perturbations in RGB channels, transform domain based methods, Quantization methods, Edge and Region based methods and more recently through deep learning methods and generative AI techniques for hiding textual information in spatial domain of images. Most of them are dependent on pixel intensity flipping over multiple pixels, such as LSB and combination of LSB based methodologies, and on transform coefficients, often resulting in the form of noise. Encoding and Decoding are deterministic in most of the existing approaches and are computationally heavy in case of higher models such as deep learning and gen AI approaches. The proposed method works on quinary pixel intensity combinations in RGB space, where five controlled different pixel intensity variations in each of the R, G, and B channels formulate up to one hundred and twenty five distinct pixel intensity combinations. These combinations are mapped to textual symbols, enabling the representation of uppercase and lowercase alphabetic characters, numeric digits, whitespace, and commonly used special characters. Different metrics such as MSE, MAE, SNR, PSNR, SSIM, Histogram Comparison and Heatmap analysis, were evaluated for both original and encoded images resulting in no significant distortion in the images. Furthermore, the method achieves improved embedding efficiency by encoding a complete textual symbol within a single RGB pixel, in contrast to LSB and MSB based approaches that typically require multiple pixels or multi-step processes, as well as transform and learning based methods that incur higher computational overhead.

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

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.