Histogram-based retrieval for encrypted JPEG images

Zhang, Xinpeng; Cheng, Hang

doi:10.1109/chinasip.2014.6889282

Cited by 20 publications

(33 citation statements)

References 3 publications

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“…For example, when recall = 0.4, our scheme with the inter-component dependency can obtain a precision of 71.1 %, a precision value about 14.2 % higher than [13]. On average, our scheme with the inter-component dependency achieves nearly 12 % improvement than [13], while our scheme can also obtain 9 % improvement without consideration of the inter-component dependency. As for the file size, we also compare our scheme with Zhang [13] over all 1000 images of the Corel image database.…”

Section: Resultsmentioning

confidence: 63%

“…4. It can demonstrate that our scheme with or without the inter-component dependency can provide a better performance than Zhang's method in [13]. For example, when recall = 0.4, our scheme with the inter-component dependency can obtain a precision of 71.1 %, a precision value about 14.2 % higher than [13].…”

Section: Resultsmentioning

confidence: 76%

“…It can demonstrate that our scheme with or without the inter-component dependency can provide a better performance than Zhang's method in [13]. For example, when recall = 0.4, our scheme with the inter-component dependency can obtain a precision of 71.1 %, a precision value about 14.2 % higher than [13]. On average, our scheme with the inter-component dependency achieves nearly 12 % improvement than [13], while our scheme can also obtain 9 % improvement without consideration of the inter-component dependency.…”

Section: Resultsmentioning

confidence: 94%

“…To make the comparison fair, we make a performance comparison of our scheme and Zhang's method [13] in terms of the supervised mechanism. The comparative results are given in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…However, the feature extraction/encryption will lead to higher computation overload and greater storage cost for users. In contrast, the authors [13] propose a histogram-based retrieval method for encrypted JPEG images, in which the feature extraction/encryption is needless. But there is one disadvantage with this scheme, namely, file size for the encrypted image will increase as discrete cosine transform (DCT) coefficients are shuffled.…”

Section: Introductionmentioning

confidence: 99%

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Markov process-based retrieval for encrypted JPEG images

Cheng

Zhang

et al. 2016

EURASIP J. on Info. Security

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This paper develops a retrieval scheme for encrypted JPEG images based on a Markov process. In our scheme, the stream cipher and permutation encryption are combined to encrypt discrete cosine transform (DCT) coefficients for protecting JPEG image content's confidentiality. And thus, it is easy for the content owner to achieve the encrypted JPEG images uploaded to a database server. In the image retrieval stage, although the server does not know the plaintext content of a given encrypted query image, he can still extract image feature calculated from the transition probability matrices related to DCT coefficients, which indicate the intra-block, inter-block, and inter-component dependencies among DCT coefficients. And these three types of dependencies are modeled by the Markov process. After that, with the multi-class support vector machine (SVM), the feature of the encrypted query image can be converted into a vector with low dimensionality determined by the number of image categories. The encrypted database images are conducted similarly. After low-dimensional vector representation, the similarity between the encrypted query image and database image may be evaluated by calculating the distance of their corresponding feature vectors. At the client side, the returned encrypted images similar to the query image can be decrypted to the plaintext images with the help of the encryption key.

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Section: Resultsmentioning

confidence: 63%

Section: Resultsmentioning

confidence: 76%

Section: Resultsmentioning

confidence: 94%

“…To make the comparison fair, we make a performance comparison of our scheme and Zhang's method [13] in terms of the supervised mechanism. The comparative results are given in Fig.…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Markov process-based retrieval for encrypted JPEG images

Cheng

Zhang

et al. 2016

EURASIP J. on Info. Security

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AC-coefficient histogram-based retrieval for encrypted JPEG images

Cheng

Zhang

2015

Multimed Tools Appl

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This paper proposes a novel retrieval scheme for encrypted JPEG images. With this scheme, the DC and AC coefficients of JPEG images are encrypted using a stream cipher and scrambling encryption, respectively. Then, the encrypted images are transmitted to and stored in a server, which can also provide retrieval service. When receiving an encrypted query image, the server without any knowledge of the plaintext content may acquire statistically its AC coefficients histogram. By calculating the distances between the histograms of encrypted query image and database image, the server may output the encrypted images closest to the query image to the authorized user.

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