基于梅林变换的视频隐写术,提高了下一代网络对深度学习隐写术的抵抗力

IF 1.6 Q2 MULTIDISCIPLINARY SCIENCES MethodsX Pub Date : 2024-08-14 DOI:10.1016/j.mex.2024.102887
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引用次数: 0

摘要

在 5 G 网络进步的时代,极强、低延迟和大容量通信的潜力为多媒体开辟了新的前景。隐写术可以将敏感数据嵌入多媒体文件,使未经授权的第三方无法读取。值得注意的是,当使用视频作为封面时,数据嵌入的容量会大大增加。隐写术的最新发展主要围绕变换域技术的改进版本。由于这种重复性,隐写分析工具更容易检测到被隐藏的数据。针对这一问题,我们的论文介绍了一种基于梅林变换的创新数据嵌入方法 MARVIS。所提方法的优越性通过 MSE、PSNR 和 SSIM 等指标得以体现。在嵌入 4 比特秘密数据时,MARVIS 的 PSNR 达到 50-60 dB,SSIM 达到 0.9998,优于其他 1 比特 40 dB 的方法。MARVIS 利用相位调制进行数据嵌入,其优势超过了使用频域进行数据嵌入的传统频域技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A Mellin transform based video steganography with improved resistance to deep learning steganalysis for next generation networks

In the era of 5 G network advancements, the potential for extremely robust, less-latency, and huge-capacity communication opens up new perspective for multimedia. Steganography enables embedding of sensitive data within multimedia files, making it unreadable to unauthorized third parties. Notably, when using videos as cover, the capacity for data embedding is substantially increased. Recent developments in steganography have largely revolved around modified versions of transform domain techniques. Due to this repetitiveness, it becomes easier for steganalytic tools in detecting concealed data. Addressing this issue, our paper introduces an innovative data embedding approach MARVIS based on the Mellin transform. The superiority of the proposed approach is exhibited using the metrics, MSE, PSNR, and SSIM. MARVIS has achieved PSNR of 50–60 dB and SSIM of 0.9998 for embedding 4 bits of secret data, outperforming other methods that achieve 40 dB for 1 bit. By quadrupling stego capacity, we can embed more secret data per pixel without compromising the integrity of the cover object.

  • MARVIS utilizes phase modulation for data embedding, offering advantages beyond traditional frequency domain techniques which use frequency domain for data embedding.

  • The effectiveness of the proposed data embedding approach is validated through Y-Net, a deep learning-based steganalysis tool.

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来源期刊
MethodsX
MethodsX Health Professions-Medical Laboratory Technology
CiteScore
3.60
自引率
5.30%
发文量
314
审稿时长
7 weeks
期刊最新文献
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