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一个端到端的卷积神经网络,通过联合加密和隐形图像来安全传输图像
Ayesha Iqbal1, Hina Sattar1, Umar Farooq Shafi2
1Department of Computer Science & IT, The Government Sadiq College Women University, Bahawalpur, 63100, Pakistan.
Scientific reports
|February 11, 2026
概括
这项研究介绍了一种新的卷积神经网络,用于安全的图像传输,将数据隐藏和转换结合起来. 该模型增强了安全性和视觉隐形性,优于传统方法.
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:
- 传统的安全图像传输方法 (加密,隐形图像) 有局限性,包括明显的文物或对攻击的脆弱性.
- 加密算法 (AES,DES) 提供安全性,但产生明显的图像噪声.
- 隐形图形图 (LSB,DCT) 是不可察觉的,但缺乏对提取攻击的强大抵抗力.
研究的目的:
- 开发一个集成的端到端卷积神经网络,用于安全的图像传输.
- 将图像转换和数据隐藏融合到一个单一的,统一的网络架构中.
- 克服传统序列管道在基于图像的通信安全方面的缺点.
主要方法:
- 一个全新的端到端卷积神经网络架构.
- 一个可学习的Key Mixer,用于适应性,内容敏感的转换.
- 一个编码器用于平滑嵌入,一个对称解码器用于准确的秘密恢复.
- 一个双损失操作平衡感知忠实度和恢复质量.
主要成果:
- 拟议的模型实现了高峰信号噪声比 (PSNR) 值> 32dB和结构相似度指数 (SSIM) 值> 0.90.
- 在STL-10数据上的实验结果表明,在质量和恢复保真性方面,其性能优于传统方法.
- 值分析表明近随机性,证明了嵌入数据的安全性.
结论:
- 开发的框架为安全的图像传输提供了可行的和可扩展的架构.
- 它通过将内容保护与视觉隐形结合起来,建立了一个新的范式.
- 该模型有效地平衡了基于图像的通信中的安全性,忠实性和不可察觉性.
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