基于边缘和局部熵融合的视觉安全性评估方法

Evaluation Method of Visual Security Based on Edge Similarity and Local Entropy Adaptive Fusion

针对基于局部熵进行加密图像视觉安全性评估存在块效应的局限性,引入图像的边缘特征,通过共有边缘来衡量加密图像与原始图像的边缘相似度,消除了块效应。由于局部熵对加密等级低的图像不敏感,边缘相似度对加密等级高的图像不敏感,将两个评估方法进行自适应融合,提出SLEES(Local Entropy and Edge Similarity,SLEES)指标。通过改变图像像素位置和图像像素值的加密方式处理图像和视频帧进行测试,实验结果表明,SLEES指标相比传统评估指标有更好的鲁棒性,评估范围更广。

Aiming at the limitation of encryption visual security based on local entropy,the edge figure of the image is proposed.The edge similarity of encrypted image is measured by the shared edge,which eliminates the blocking effect of local entropy.Since the local entropy is insensitive to images with low encryption level and the edge similarity is insensitive to images with high encryption level,the two evaluation methods have a self-adaptive convergence and the index SLEES(Local Entropy and Edge Similarity,SLEES)is proposed.The images and video frames are processed by changing the pixel position and pixel value and then are tested.The experimental results indicate that the proposed index SLEESis more robust than the traditional evaluation indexes,and the evaluation range is wider.