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通过卷积神经网络和自适应性损失优化来进行可靠的数据隐藏的深度隐藏方法
1Department of Computer Science and Engineering, Amrita School of Computing, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India. p_malathy@cb.amrita.edu.
Scientific reports
|December 23, 2025
概括
本研究介绍了使用卷积神经网络 (CNN) 进行强大的数据隐藏的深度隐藏图框架. 拟议的损失函数3 (LF3) 实现了高有效载荷和对各种攻击的强烈抵抗力,优于基于GAN的方法.
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:
- 隐藏图对于安全的数据传输至关重要.
- 现有的方法在有效载荷能力和稳定性方面面临挑战.
- 深度学习为先进的石图解决方案提供了潜力.
研究的目的:
- 为强大的数据隐藏提出一个新的深度隐藏图框架.
- 使用专门的损失函数来优化框架.
- 根据最先进的方法评估框架的性能.
主要方法:
- 开发了一个三层的卷积神经网络 (CNN) 架构,包括准备,隐藏和揭示网络.
- 准备网络使用过器来提取边缘特征.
- 隐藏网络采用了自适应嵌入,优化了四个损失功能,特别是损失功能3 (LF3).
主要成果:
- 损失函数3 (LF3) 实现了每像素3-5位的有效载荷,并改善了峰值信号噪声比 (PSNR).
- 框架表现出对高斯噪声,裁剪和旋转的强度.
- 在各种steganalysis技术 (组图,统计,基于CNN) 中观察到较低的检测率.
结论:
- 建议使用LF 3的深度隐形图框架提供了有效载荷和强度之间的卓越平衡.
- 它在效率和性能方面优于基于生成对抗网络 (GAN) 的方法.
- 这一进步对包括医学成像在内的安全数据隐藏应用有希望.
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