MIRA: Multi-scale invertible dual-attention redundancy-aware network for high-capacity video steganography.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 42142409.
- Also identified by DOI 10.1016/j.neunet.2026.109090.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Achieving high embedding capacity while maintaining visual imperceptibility and strict reversibility remains a critical challenge in deep video steganography. Most existing invertible neural networks suffer from limited feature representation and inadequate modeling of spatio-temporal redundancy. To address these limitations, we propose MIRA, a Multi-scale Invertible Dual-Attention Redundancy-Aware Network. The framework incorporates a Multi-scale Hybrid Invertible Block to enhance global-local feature fusion and introduces a Dual-Attention Redundancy Refinement Module (DARRM). Within DARRM, we synergize local convolutional attention with non-local sparse attention to explicitly capture spatio-temporal redundancies, enabling fine-grained, content-adaptive feature optimization. Extensive experiments on the Vimeo-90K, UCF-101, and DAVIS datasets demonstrate that MIRA consistently achieves state-of-the-art performance. On Vimeo-90K, our method yields a stego video PSNR of 45.25 dB, a recovered secret video PSNR of 48.45 dB, and a Temporal Consistency Error (TCE) of 0.98×10<sup>-3</sup>, with a recovery Mean Absolute Error (MAE) of 0.68. Moreover, the proposed framework exhibits superior zero-shot generalization and comprehensive resilience against diverse real-world distortions and steganalysis attacks. This work provides a highly efficient, secure, and robust solution for practical video steganography.