PEID: A Perceptually Encrypted Image Database for Visual Security Evaluation

Guo, Shangwei; Xiang, Tao; Li, Xiaoguo; Yang, Ying

doi:10.1109/tifs.2019.2935415

Cited by 24 publications

(18 citation statements)

References 39 publications

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“…SRCC and KRCC can evaluate performance monotonicity, PLCC can evaluate linearity, and RMSE can evaluate accuracy. Before the calculation of the correlation between the subject VSA scores and object scores MOS, a five-parameter logistic regression function is applied to reduce the nonlinearity of the subject VSA scores [33], which is defined as…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…For example, an image with a higher VIF, PSNR, or SSIM may even be more visually secure than one with a lower value of one of these indices. Figures 1(a)-1(c) show the performance of the PSNR, VIF, and SSIM indices on several images from the PEID database [33]. Figure 1(c) has a higher visual security, but this image is found to have better visual quality as assessed using the PSNR, VIF, and SSIM.…”

Section: Image Quality Assessmentmentioning

confidence: 99%

“…To verify the performance of our proposed method, experiments are conducted on two common encrypted databases: IVC-SelectEncrypt [32] and PEID [33]. e IVC-SelectEncrypt can be downloaded from http:// www.polytech.univ-nantes.fr/autrusseau-f/Databases/Selective Encryption/.…”

Section: Test Databasementioning

confidence: 99%

“…Flowchart illustration of the proposed security model for selective encrypted images. e performances of IQAs on images from the IVC-SelectEncrypt database[32] and PEID[33] database. (a) Original image from the PEID database.…”

mentioning

confidence: 99%

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Visual Security Assessment via Saliency-Weighted Structure and Orientation Similarity for Selective Encrypted Images

Zheng-guo

Zhang

Ren

et al. 2021

Security and Communication Networks

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Selective encryption has been widely used in image privacy protection. Visual security assessment is necessary for the effectiveness and practicability of image encryption methods, and there have been a series of research studies on this aspect. However, these methods do not take into account perceptual factors. In this paper, we propose a new visual security assessment (VSA) by saliency-weighted structure and orientation similarity. Considering that the human visual perception is sensitive to the characteristics of selective encrypted images, we extract the structure and orientation feature maps, and then similarity measurements are conducted on these feature maps to generate the structure and orientation similarity maps. Next, we compute the saliency map of the original image. Then, a simple saliency-based pooling strategy is subsequently used to combine these measurements and generate the final visual security score. Extensive experiments are conducted on two public encryption databases, and the results demonstrate the superiority and robustness of our proposed VSA compared with the existing most advanced work.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Image Quality Assessmentmentioning

confidence: 99%

Section: Test Databasementioning

confidence: 99%

mentioning

confidence: 99%

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Visual Security Assessment via Saliency-Weighted Structure and Orientation Similarity for Selective Encrypted Images

Zheng-guo

Zhang

Ren

et al. 2021

Security and Communication Networks

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“…For the development of visual quality indices for mid-to lowquality images there are, to the best of the authors knowledge, two databases: [16] and [17]. This is not an optimal situation but at least allows for the development on one database and the testing on the other.…”

Section: Introductionmentioning

confidence: 99%

Low Quality and Recognition of Image Content

Hofbauer

Autrusseau

Uhl

2022

IEEE Trans. Multimedia

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Assessment of visual encryption of video and image content requires a reliable estimation of content recognizability and low quality. As pointed out in the literature, current methods are insufficient and research into this topic, as well as into the relation between low quality and recognizability, is still lacking. This lack of research is primarily due to a lack of data. To improve on the status-quo we have taken a recognizability database and performed a subjective quality evaluation on a subset of the images. This gives us a new database with both subjective recognizability and quality information and allows to delve into the relation between low quality and recognizability. We analyze the relationship between quality and recognizability as well as the predictive quality of state of the art visual quality indices. We show that the visual quality indices are poor indicators for the estimation of recognizability. Furthermore, we show that they must be a poor fit because of the disparity between two distinct perceptual tasks: quality and recognizability.

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