DiSCoQR: Diffusion-Driven Semantic Compression for Robust Image Steganography in Standard QR Codes

Secure and undetectable transmission of visual intelligence through open networks presents a critical technological challenge in today's heavily monitored digital world. Text-based encryption is insufficient for massive, multimodal data from ubiquitous devices. The fundamental dilemma lies in transmitting large-scale visual data through noisy, adversarial channels while ensuring seamless concealment and robust recovery. In this paper, we bridge the gap by combining diffusion models with the universal presence and robustness of Quick Response (QR) codes for covert communication. Grounding steganographic security in the normative patterns of machine-readable symbols decouples it from the statistical properties of natural images. The reconstructions reside on the same semantic manifold as the originals, ensuring their functional equivalence in downstream tasks and maintaining robustness against quality degradation that would cripple pixel-based methods. Our training-free model compresses complex visual information into binary representations that integrate seamlessly into the data region of standard-compliant QR codes. This approach facilitates covert data transmission while preserving the full functionality and readability of the QR container. We achieve a compression ratio exceeding 400:1 while retaining semantic information for high-quality reconstruction. The preservation of all functional QR code components ensures complete compatibility with standard readers while concealing substantial visual content. Experimental results show a superior balance of security and performance compared to existing methods, all of which require no extra training.