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Visual Security Evaluation of Perceptually Encrypted Images Based on Image Importance

机译:基于图像重要性的感知加密图像的视觉安全评估

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摘要

Perceptual/selective encryption has been gaining widespread attention as an emerging technology for image privacy protection. However, few studies focus on the visual security evaluation of perceptually encrypted images, which has a significant impact on measuring the effectiveness and practicality of these encryption methods. In this paper, we propose an image importance-based visual security index (IIBVSI) by leveraging spatial contrast and texture features. Based on the characteristics of perceptually encrypted images, we present an averaged high-order gradient magnitude map to describe the spatial contrast feature and introduce a combined local amplitude map of multiple log-Gabor filters to represent the texture feature. Specifically, the multiresolution representation of an image is first created by downsampling to simulate the hierarchical property of the human visual system. Next, for each scale of image resolution, the spatial contrast and the texture feature maps are extracted from both plain and encrypted images. Similarity measurements are then conducted on these feature maps to generate the contrast and the texture similarity maps. An image importance-based pooling strategy is subsequently proposed to combine these measurements and generate a visual security score. The final IIBVSI score is computed by averaging the visual security scores of all scales of image resolution. Extensive experiments are conducted on several publicly available databases, and the results demonstrate the superiority and robustness of our proposed IIBVSI compared with existing state-of-the-art work in the low and moderate image quality ranges.
机译:感知/选择性加密一直受到广泛的关注,作为图像隐私保护的新兴技术。然而,很少有研究专注于对感知加密图像的视觉安全评估,这对测量这些加密方法的有效性和实用性产生了重大影响。在本文中,我们通过利用空间对比度和纹理特征提出基于图像的视觉安全索引(IIBVSI)。基于感知加密图像的特征,我们呈现了平均的高阶梯度幅度图来描述空间对比度特征,并引入多个记录型滤波器的组合局部幅度映射以表示纹理特征。具体地,首先通过下采样来模拟人类视觉系统的分层属性来创建图像的多分辨率表示。接下来,对于每种图像分辨率,空间对比度和纹理特征映射是从平原和加密图像中提取的。然后在这些特征映射上进行相似度测量以产生对比度和纹理相似度图。随后提出了一种基于图像的池池策略来组合这些测量并生成视觉安全分数。通过平均图像分辨率的所有尺度的视觉安全分数来计算最终的IIBVSI分数。在若干公开的数据库上进行了广泛的实验,结果表明了我们所提出的IIBVSI的优势和稳健性,而在低和中等图像质量范围内与现有的最先进的工作相比。

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