A privacy-preserving content-based image retrieval method based on deep learning in cloud computing

Ma, Wentao; Zhou, Tongqing; Qin, Jiaohua; Xiang, Xuyu; Tan, Yun; Cai, Zhiping

doi:10.1016/j.eswa.2022.117508

Cited by 19 publications

(2 citation statements)

References 31 publications

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“…A multi-class support vector machine (M-SVM) is used for the classification. Ma, W., et al [22] suggested a cloud-based privacy-preserving image retrieval service using deep convolutional features with from the encrypted image. For image encryption, a hybrid encryption method is adopted.…”

Section: IImentioning

confidence: 99%

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Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Palai¹,

Jena²,

Pattanaik³

et al. 2023

IJACSA

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Recent development of digital photography and the use of social media using smartphones has boosted the demand for image query by its visual semantics. Content-Based Image Retrieval (CBIR) is a well-identified research area in the domain of image and video data analysis. The major challenges of a CBIR system are (a) to derive the visual semantics of the query image and (b) to find all the similar images from the repository. The objective of this paper is to precisely define the visual semantics using hybrid feature vectors. In this paper, a CBIR system using encoded-based feature fusion is proposed. The CNN encoding features of the RGB channel are fused with the encoded texture features of LBP, CSLBP, and LDP separately. The retrieval performance of the different fused features is tested using three public datasets i.e. Corel-lK, Caltech, and 102flower. The result shows the class properties are better retained using the LDP with RGB encoded features, this helps to enhance the classification and retrieval performance for all three datasets. The average precision of Corel-lK is 94.5% and it is 89.7% for Caltech, and 88.7% for the 102flower. The average f1-score is 89.5% for Caltech, and 88.5% for the 102flower. The improvement in the f1-score value implies the proposed fused feature is more stable to deal the class imbalance problem.

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Section: IImentioning

confidence: 99%

“…With the implementation of Deep learning features a new breakthrough is achieved in the field of computer vision and its applications. It uses the Convolutional neural networks (CNNs) features [14,[20][21][22][23] as the image descriptor. The Deep learning technique requires adequate images for its training.…”

Section: Introductionmentioning

confidence: 99%

Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Palai¹,

Jena²,

Pattanaik³

et al. 2023

IJACSA

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Development of scalable coding on encrypted images using BTC for different non-overlapping block size

Pankiraj

Govindaraj

Zhang

et al. 2023

SIViP

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Development of scalable coding of encrypted images using Modified Absolute Moment Block Truncation Code

Pankiraj,

Govindaraj,

Zhang

et al. 2024

SIViP

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Many researchers worked on scalable coding for unencrypted images, and there is more space for research in scalable coding for encrypted images. This paper proposes a novel method of scalable coding for encrypted images, especially for lossy compression images using the Modified Absolute Moment Block Truncation Code (MAMBTC) technique. The given input image is compressed using MAMBTC and then encrypted using a Pseudo-Random Number (PRNG) at the encryption phase. The PRNG is shared between the encoder and the decoder. At the decryption phase, the compressed pixel value is obtained by decryption using the PRNG and then reconstructed using MAMBTC, scaled by scaling factor 2 and Bilinear Interpolation Technique to obtain the original image. MAMBTC gives better image quality than Block Truncation Code (BTC), a higher PSNR of 36.32 dB, and a Compression ratio of 1.09, which makes the proposed system ready for the signal processing community/applications.

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A privacy-preserving content-based image retrieval method based on deep learning in cloud computing

Cited by 19 publications

References 31 publications

Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Content-Based Image Retrieval using Encoder based RGB and Texture Feature Fusion

Development of scalable coding on encrypted images using BTC for different non-overlapping block size

Development of scalable coding of encrypted images using Modified Absolute Moment Block Truncation Code

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