Latent Space-based Coverless Steganography with Encryption and Multi-Metric Security Evaluation

Abstract

This research introduces a coverless steganography framework that embeds secret messages within the latent space of images produced by a generative adversarial network (GAN). It facilitates clandestine transmission without altering any pre-existing cover media. The sender encodes the message into a 512-dimensional latent vector utilizing binary encoding with a 16-bit header, optionally employing XOR encryption to augment confidentiality. An picture generated from the encoded latent vector is conveyed as the carrier, maintaining the coverless principle and enhancing resistance to steganalysis. Experimental findings demonstrate consistent end-to-end recovery for brief messages (e.g., "OK," “AI DEMO”), minimal transmission overhead (sub-kilobyte payloads), and effective decoding (average ≈0.22 s). The approach accommodates messages with up to 37 characters and maintains robustness under JPEG compression within the evaluated parameters. A comprehensive review utilizing many metrics, enhanced by automated visual analytics, offers a clear assessment of security, efficiency, and reconstruction quality. The proposed technique provides a viable, reproducible, and scalable basis for AI-driven coverless covert communication.