GCL-MIH:一种基于生成的无盖多图像隐藏方法
IEEE transactions on pattern analysis and machine intelligence
|January 29, 2026
概括
本研究介绍了一种基于生成的无盖多图像隐藏方法 (GCL-MIH),用于安全和高容量的秘密传输. 通过生成真实的面部图像来隐藏多个秘密图像而没有扭曲,GCL-MIH实现了高安全性和容量.
科学领域:
- 计算机科学 计算机科学
- 信息安全 信息安全
- 数字图像处理 数字图像处理
背景情况:
- 现有的图像隐藏方法在容量和安全/扭曲之间进行权衡.
- 基于覆盖的方法提供高容量,但有风险扭曲形象和安全漏洞.
- 安全的无遮盖方法提供了良好的安全性,但由于数据嵌入能力有限而受到影响.
研究的目的:
- 提出一种新的基于生成的无盖多图像隐藏方法 (GCL-MIH),以提高安全性和容量.
- 为了在单个生成的图像中隐藏多个秘密图像,而不需要预定义的封面图像.
- 通过同时实现高容量和高安全性来克服现有方法的局限性.
主要方法:
- 一个特征反向模块将多个秘密图像压缩成特征向量.
- 矢量被规范化,以符合标准正常分布.
- 一个可逆生成网络 (Flow-GAN) 生成一个现实的面部图像,嵌入秘密信息.
主要成果:
- 在一个生成的面部图像中,GCL-MIH成功地隐藏了多达四个秘密图像.
- 实现了每像素32位 (bpp) 的最大嵌入速率,明显超过现有的无盖方法.
- 生成的stego图像表现出高现实性 (FID分数:11.98),恢复的图像显示出令人满意的真实性 (平均). 在PSNR:33.18 dB,平均值. 在SSIM: 0.9412).
结论:
- 拟议的GCL-MIH方法有效地解决了当前图像隐藏技术的局限性.
- 它提供了一种有前途的解决方案,用于使用生成模型进行安全和高容量的秘密信息传输.
- 该方法在产生的图像的容量,安全性和真实性方面表现出卓越的性能.
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