Perceptual visual security assessment by fusing local and global feature similarity

Selective encryption is an effective technique for multimedia big data encryption. A perceptual metric that takes the human visual system (HVS) into account is necessary for evaluating the visual security degrees of selectively encrypted images. Existing metrics are mainly based on the similarity of structural information which is represented by local spatial contrast features. However, visual security is concerned with not only the distortion of structural information but also the leakage of important visual information. The local features cannot exactly express the leakage of important visual information. This paper presents a perceptual visual security assessment metric by fusing local and global feature similarity. Considering the HVS response, the proposed metric measures in three aspects: the distortion of structural information, the leakage of important visual information, and the changes of frequency components. To measure the distortion of structure information, local pattern similarity is calculated based on the normalized Hamming distance between the local binary pattern (LBP) binary codes. The similarity of the global LBP histogram is computed to evaluate the leakage of important visual information. A lowpass weighted discrete cosine transform (DCT) frequency similarity is presented to detect the changes of various frequency components. Experimental results demonstrate that the proposed metric achieves significantly higher performance and stronger robustness than the state-of-the-art metrics.

Automated summary

This paper proposes a perceptual visual security assessment metric by fusing local and global feature similarity, which combines the distortion of structural information, leakage of important visual information, and changes in frequency components. Experimental results show that the proposed metric achieves significantly higher performance and robustness than existing metrics.