Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

Yang, Hengfu; Yin, Jianping; Yang, Ying

doi:10.1109/access.2019.2911207

Cited by 14 publications

(6 citation statements)

References 40 publications

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“…Tang's method shows good results in the application of image quality assessment, but it can only resist rotation within 5°. Yang et al [34] used low-rank decomposition and LBP to generate a hash. Yang's method can only resist rotation of 3°.…”

Section: Related Workmentioning

confidence: 99%

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Liang

Tang

Huang

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Image copy detection is an important technology of copyright protection. This paper proposes an efficient hashing method for image copy detection using 2D-2D (two-directional two-dimensional) PCA (Principal Component Analysis). The key is the discovery of the translation invariance of 2D-2D PCA. With the property of translation invariance, a novel model of extracting rotation-invariant lowdimensional features is designed by combining PCT (Polar Coordinate Transformation) and 2D-2D PCA. The PCT can convert an input rotated image to a translation matrix. Since the 2D-2D PCA is invariant to translation, the low-dimensional features learned from the translation matrix are rotation-invariant. Moreover, vector distances of low-dimensional features are stable to common digital operations and thus hash construction with the vector distances is of robustness and compactness. Three open image datasets are exploited to conduct various experiments for validating efficiencies of the proposed method. The results demonstrate that the proposed method is much better than some representative hashing methods in the performances of classification and copy detection.

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Section: Related Workmentioning

confidence: 99%

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Liang

Tang

Huang

et al. 2023

IEEE Trans. Knowl. Data Eng.

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“…It may be used for privacy-preserving feature extraction in a remote cloud server hosted by the third party. In recent years, researchers have studied local binary patterns (LBP) computation [38] in the encrypted domain. Sultana and Shubhangi presented a privacy-preserving LBP computation outsourcing protocol (PP-LBP) that enables extracting LBP features from encrypted images [20].…”

Section: Related Workmentioning

confidence: 99%

Secure Outsourcing Algorithm of BTC Feature Extraction in Cloud Computing

Jiang

Yang

2020

IEEE Access

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Advances in cloud computing have aroused many researchers' interest in privacy-preserving feature extraction over outsourced multimedia data, especially private image data. Since block truncation coding (BTC) is known as a simple and efficient technology for image compression, this paper focuses on privacy-preserving feature extraction in BTC compressed domain. We propose a privacy-preserving computation of BTC feature extraction over massive encrypted images (also called PPBTC). First, all images are uploaded to the cloud after encryption. The privacy-preserving image encryption process consists of block permutation, pixel diffusion, and a bit-plane random shift. BTC features remain unchanged after encryption and the cloud server can directly extract BTC features from the encrypted images. Some analyses and experimental results demonstrate that the proposed privacy-preserving feature extraction scheme for BTCcompressed images is efficient and secure, and it can be applied to secure image computation applications in cloud computing. INDEX TERMS Feature extraction, privacy-preserving, block truncation coding, homomorphic encryption.

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“…Some analyses and experimental results demonstrate that the proposed privacy-preserving feature extraction scheme for BTC-compressed images is efficient and secure, and it can be used to secure image computation applications in cloud computing. A local sensitive hash algorithm was employed to produce the searchable index because of the wide application of image hash [22] in image retrieval. The chaotic encryption approach was used to protect the security of images and indexes.…”

Section: Introductionmentioning

confidence: 99%

A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

Wang¹,

Jiang²,

Qin³

et al. 2021

Computers, Materials &Amp; Continua

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In the era of big data, outsourcing massive data to a remote cloud server is a promising approach. Outsourcing storage and computation services can reduce storage costs and computational burdens. However, public cloud storage brings about new privacy and security concerns since the cloud servers can be shared by multiple users. Privacy-preserving feature extraction techniques are an effective solution to this issue. Because the Rotation Invariant Local Binary Pattern (RILBP) has been widely used in various image processing fields, we propose a new privacy-preserving outsourcing computation of RILBP over encrypted images in this paper (called PPRILBP). To protect image content, original images are encrypted using block scrambling, pixel circular shift, and pixel diffusion when uploaded to the cloud server. It is proved that RILBP features remain unchanged before and after encryption. Moreover, the server can directly extract RILBP features from encrypted images. Analyses and experiments confirm that the proposed scheme is secure and effective, and outperforms previous secure LBP feature computing methods.

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Robust Image Hashing Scheme Based on Low-Rank Decomposition and Path Integral LBP

Cited by 14 publications

References 40 publications

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Efficient Hashing Method Using 2D-2D PCA for Image Copy Detection

Secure Outsourcing Algorithm of BTC Feature Extraction in Cloud Computing

A Secure Rotation Invariant LBP Feature Computation in Cloud Environment

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