Hu, KunHu, ZixuanZhu, QianhuiWang, XiaochaoWang, XingjunChen, RenjieRitschel, TobiasWhiting, Emily2024-10-132024-10-132024978-3-03868-250-9https://doi.org/10.2312/pg.20241280https://diglib.eg.org/handle/10.2312/pg20241280Current video steganography frameworks have difficulties in balancing robustness and imperceptibility at high resolution. To achieve better video coherence, robustness, and invisibility, we propose an efficient high-resolution video steganography method, named StegaVideo, that utilizes temporal guidance and edge guidance techniques. StegaVideo particularly focuses on concentrating the embedding message in the edge region to enhance invisibility, achieving a Peak Signal to Noise Ratio (PSNR) value of over 38 dB. We simulate various attacks to enhance robustness, with an average bit accuracy of above 99.5%. We use a faster embedding and extracting network, resulting in a 10× improvement in inference speed. Our method outperforms current leading video steganography systems in terms of efficiency, robustness, resolution, and inference speed, as demonstrated by the experiment. Our code will be publicly available at https://github.com/LittleFocus2201/StegaVideo.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Video processing; Computer vision; Video SteganographyComputing methodologies → Video processingComputer visionVideo Steganography10.2312/pg.2024128012 pages