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基于深度学习的图像隐形图像与潜空间嵌入和智能解码器选择
Yiqiao Zhou1, Na Wang1, Xiaolong Hong1
1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Entropy (Basel, Switzerland)
|December 24, 2025
概括
这项研究引入了图像隐形图像的新型深度学习框架,通过提高秘密恢复精度和隐形图像质量来增强安全通信,同时降低可检测性.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 人工智能的人工智能
背景情况:
- 传统的LSB隐形图是可以检测的.
- 深度学习方法 (GAN,自动编码器) 是有前途的,但缺乏适应性和稳定性.
- 现有的模型面临着各种数据,有限的数据集和扭曲弹性方面的挑战.
研究的目的:
- 开发一个灵活而强大的图像隐形图像框架.
- 为了提高秘密恢复精度 (SRA) 和静态图像质量 (SSIM,PSNR).
- 为了增强对steganalysis和噪声扭曲的弹性.
主要方法:
- 提出了一个灵活的框架,具有自适应的多编码器解码器对.
- 采用了广泛的数据集培训和优化的架构.
- 集成的专用组件,以提高性能.
主要成果:
- 在秘密恢复精度 (SRA) 和静态图像质量 (SSIM,PSNR) 中取得了显著的改进.
- 已证明对噪声的高稳定性,SSIM达到0.99和恢复精度超过98%.
- 降低检测率,在稳定分析中AUC接近0.5.
结论:
- 拟议的框架为安全的图像传输设定了一个新的基准.
- 在数据隐藏,图像质量和不可检测性方面实现了卓越的性能.
- 提供增强的隐私保护通信功能.
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