A Content-Based Image Retrieval Scheme Using an Encrypted Difference Histogram in Cloud Computing

Liu, Dandan; Shen, Jian; Xia, Zhihua; Sun, Xingming

doi:10.3390/info8030096

Cited by 26 publications

(20 citation statements)

References 26 publications

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“…Inspired by those outsourced CBIR schemes, we propose a secure retrieval scheme with the combination of AC-coefficients and color histograms in the encrypted domain. The retrieval result shows that the combination of them outperforms individual utilization, and our retrieval accuracy is six percent higher than the proposed scheme in Liu et al [Liu, Shen, Xia et al (2017)].…”

Section: Introductionmentioning

confidence: 60%

“…In this scheme, color information is protected by random permutation encryption and the rows and columns are disorganized to preserve texture information of images. Liu et al [Liu, Shen, Xia et al (2017)] proposed an image retrieval scheme based on difference histogram. In their scheme, two kinds of difference matrices (order difference and random order difference) are proposed, and the value replacement and location scrambling are utilized to encrypt the difference matrix.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Xia

Lu²,

Qiu³

et al. 2019

Computers, Materials &Amp; Continua

Self Cite

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Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this scheme, the discrete cosine transform (DCT) is performed on the Y component. The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling. Then the image owner transmits the encrypted images to the cloud server. When receiving a query trapdoor form on query user, the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components. The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms. Finally, the encrypted images closest to the query image are returned to the query user. ]. Nevertheless, huge storage space and complex computation requirements are needed to search one particular image from a large amount of images, which is almost impossible for users with lightweight devices (e.g. smart phones), so outsourcing image data to the cloud storage providers becomes one of the most convenient options because they provides enormous storage space and powerful computing ability. However, outsourcing the image data with sensitive information (such as financial position, personal identification and healthy records) to the server often results in great challenges in terms of data control and privacy. To prevent unauthorized access, the image owners usually encrypt their image data before its transmission to the server, but the conventional encryption operations pose a threat to image retrieval. For effectively utilizing and managing image resources, the researchers have made great efforts and they have proposed many practical retrieval schemes in this field [Reynolds (2016); Parikesit ], the image owners not only complete the task of index construction and encryption, but transmit the encrypted index to the server as well. Once receiving the query trapdoors, the server retrieves the intended images and returns them to the query users. These schemes provides feasible solutions and realize secure image retrieval in the encrypted domain, but the computation burden of index generation and encryption in the image client is really too heavy. To reduce the workload of image client and effectively implement retrieval work, some content-based image retrieval (CBIR) schemes ...

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Section: Introductionmentioning

confidence: 60%

Section: Introductionmentioning

confidence: 99%

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Xia

Lu²,

Qiu³

et al. 2019

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…Furthermore, we can see that the retrieval accuracy on Corel-1000 image database encrypted by DERCA-CBIR 23 is better than retrieval accuracy on that encrypted by our work due to the fixed rule and iteration time are used in f P and f G in DERCA-CBIR. 23 Meanwhile, our work realizes a higher retrieval accuracy compared with EODH-CBIR. 23 Therefore, the retrieval accuracy of encrypted images in our work is reasonable and acceptable if high retrieval accuracy is unnecessary.…”

Section: Comparisonmentioning

confidence: 75%

“…Considering the color characteristics can be effectively utilized for image retrieval, Ferreira et al 21,22 used a one-to-one deterministic encryption algorithm to encrypt the pixels in the image to enable CBIR of the encrypted images. Liu et al 23 processed the original images into the encrypted difference matrices. The cloud server can extract encrypted difference histograms as image features and evaluate the similarity.…”

Section: Privacy-preserving Image Retrievalmentioning

confidence: 99%

Privacy‐preserving image retrieval in a distributed environment

Zhou

Qin

Hou

et al. 2022

Int J of Intelligent Sys

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Nowadays, several image-based smart services have been widely used in our daily lives, generating many digital images. Since smart devices outsource digital images to the cloud, researchers prefer to select some desired targets from the massive images within the cloud for analysis and improve smart services. Therefore, protective image retrieval on the cloud has attained maximum concentration for privacy-preserving purposes, and the availability assurance of images on the cloud is also a crucial link. Ensuring image security and availability in the cloud environment and precisely preserving retrieval accuracy is comes as a utilitysecurity dilemma while few existing works have explicitly addressed it. Therefore, this paper proposes privacy-preserving image retrieval in the distributed environment based on the combination of image encryption for similarity search and secret image sharing. On the basis of them, we define two-stage encryption. The first-stage encryption algorithm is introduced by modifying Wolfram's reversible cellular automata-based image encryption, which can create a set of processing images to ensure image security and retrieval accuracy. Then, the second-stage encryption algorithm is put forward based on secret image sharing to improve image security and availability. The color histogram could be extracted from the encrypted images for similarity retrieval, and the shadows could be

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“…In the early image retrieval methods, text-based image retrieval (TBIR) 8 , 9 follows the traditional text annotation technology to implement retrieval by text matching. In content-based image retrieval (CBIR) 10 , 11 , with the help of the computer to explore image content features and take it as clues to detect other images with similar features from image database. However, TBIR and CBIR need a great quantity manual operation and computational resources.…”

Section: Introductionmentioning

confidence: 99%

Deep parameter-free attention hashing for image retrieval

Yang

Wang

Cheng

2022

Sci Rep

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Deep hashing method is widely applied in the field of image retrieval because of its advantages of low storage consumption and fast retrieval speed. There is a defect of insufficiency feature extraction when existing deep hashing method uses the convolutional neural network (CNN) to extract images semantic features. Some studies propose to add channel-based or spatial-based attention modules. However, embedding these modules into the network can increase the complexity of model and lead to over fitting in the training process. In this study, a novel deep parameter-free attention hashing (DPFAH) is proposed to solve these problems, that designs a parameter-free attention (PFA) module in ResNet18 network. PFA is a lightweight module that defines an energy function to measure the importance of each neuron and infers 3-D attention weights for feature map in a layer. A fast closed-form solution for this energy function proves that the PFA module does not add any parameters to the network. Otherwise, this paper designs a novel hashing framework that includes the hash codes learning branch and the classification branch to explore more label information. The like-binary codes are constrained by a regulation term to reduce the quantization error in the continuous relaxation. Experiments on CIFAR-10, NUS-WIDE and Imagenet-100 show that DPFAH method achieves better performance.

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A Content-Based Image Retrieval Scheme Using an Encrypted Difference Histogram in Cloud Computing

Cited by 26 publications

References 26 publications

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

A Privacy-Preserving Image Retrieval Based on AC-Coefficients and Color Histograms in Cloud Environment

Privacy‐preserving image retrieval in a distributed environment

Deep parameter-free attention hashing for image retrieval

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