Implicit video steganography

Abstract Deep learning-based video steganography has made significant strides, yet conventional explicit methods often suffer from cover distortion and reduced extraction accuracy at high capacities. In this paper, we propose an implicit video steganography framework that treats video hiding and recovery as a dual-stream generation process leveraging implicit neural representations. Instead of altering existing carriers, secret information is encoded within the neural network’s weights, making it an inherent part of the generation process. We introduce a dual-stream input encoding mechanism that decouples the input space into temporal and cryptographic encodings to ensure covert transmission, allowing only authorized receivers to recover hidden content. Furthermore, a multi-scale generation network, incorporating frequency-aware upscaling and statistical distribution loss, is presented to achieve high-quality reconstruction. Extensive experiments demonstrate that our approach achieves state-of-the-art results, minimizing detectable discrepancies while concealing up to seven secret videos within a single carrier. Our method significantly outperforms existing benchmarks by a margin of over 10 dB in peak signal-to-noise ratio, highlighting its superior imperceptibility, accuracy, and security.