【摘要】 现有置乱评价方法大多与像素位置有密切关系,容易受到剪切、旋转等有意或无意攻击的影响,导致误差较高。针对该问题,提出一种基于提升小波、曼哈顿距离和纹理进行置乱程度评价的方法。对置乱前后图像分别进行提升小波变换,求对应系数的统计距离,高频生成灰度共生矩阵,提取纹理特征,通过距离和纹理求出置乱度。实验结果表明,与现有位置相关的置乱方法相比,该方法适用范围更广,与主观评价更一致,对原图像的依赖性更小,能更有效地用于置乱程度评价。更多还原

【Abstract】 Existing image scrambling evaluation methods usually are closely associated with pixel location,and are vulnerable to the influence of intentional and unintentional attacks,such as shear and rotation,and have a high error. To solve this problem,an image scrambling degree evaluation method based on lifting wavelet,Manhattan distance,and texture is proposed. The images before and after scrambling are transformed by lifting wavelet respectively; the statistical distance of corresponding wavelet coefficient is calculated; the Gray Level Co-occurrence Matrix( GLCM) is generated with high-frequency; the texture features are extracted; and the scrambling degree is obtained by distance and texture.Experimental results show that,compared with the existing scrambling methods based on location,this method has a wider application range,is more consistent with the subjective evaluation,less reliant on the original image,and more effective in scrambling degree evaluating.更多还原