卷积神经网络和波形组合对抗几何攻击
C Lakshmi1, C Nithya1, R Sivaraman2
1School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur, 613 401, India.
Scientific reports
|August 26, 2025
概括
这项研究引入了一种新的三层图像水印技术,使用离散波形转换 (DWT) 进行电子治理的安全认证. 该方法嵌入加密的标志和所有者身份,通过卷积神经网络 (CNN) 恢复显示出高强度和透明度.
科学领域:
- 数字水印
- 信息安全
- 图像处理
背景情况:
- 电子治理系统需要对银行,医疗和保险等服务进行强有力的所有权识别.
- 传统的水印方法在平衡有效载荷能力,稳定性和感知透明度方面面临挑战.
- 验证对于数字环境中的安全数据交换至关重要.
研究的目的:
- 在转换领域提出一个新的三层,特征依赖的图像水印方案.
- 加强数字电子治理应用中的安全性和身份验证.
- 为了实现高负载能力,感知透明度和对各种攻击的强度.
主要方法:
- 使用离散波形转换 (DWT) 开发了一种三层水印方法.
- 一个加密标志的单数值,文字签名的算术编码和所有者身份的运行长度编码嵌入了不同的DWT分解级别.
- 使用特征提取和数据压缩技术进行嵌入.
- 卷积神经网络 (CNN) 用于封面图像的可逆性和水印提取.
- 模拟了包括高斯噪音,盐和胡噪音,旋转和裁剪在内的攻击.
主要成果:
- 拟议的方案实现了高度的感知透明度,结构相似性指数 (SSIM) 和规范化相关性 (NC) 接近统一.
- 证明了对各种攻击的强度,峰值信号与噪声比 (PSNR) 约为44dB.
- 高SSIM (>0.99) 和NC (~1.0) 值证实了增强的感知透明度和稳定性.
- 攻击后的最小比特错误率表明可靠的水印恢复与CNN辅助恢复.
- 在封面图像恢复过程中实现了低平均平方误差 (MSE).
结论:
- 开发的基于DWT的水印系统有效地嵌入了大量的有效载荷,同时保持了高度的感知透明度和稳定性.
- 整合CNN显著提高了该计划对各种攻击和帮助准确的图像恢复的弹性.
- 这种方法为电子治理和其他敏感数字应用中的安全认证提供了有希望的解决方案.
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