Deep Discrete Supervised Hashing

Jiang, Qing-Yuan; Cui, Xue; Li, Wu-Jun

doi:10.1109/tip.2018.2864894

Cited by 107 publications

(72 citation statements)

References 33 publications

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“…In most schemes, the index encryption module uses the LSH algorithm. Some hashing algorithms [20][21][22][23] based on supervision and quantization are proposed to evaluate the quality of hash code learning. These hash algorithms are optimized primarily from the perspective of network structure and the loss function.…”

Section: Index Encryptionmentioning

confidence: 99%

See 1 more Smart Citation

A Privacy-Protected Image Retrieval Scheme for Fast and Secure Image Search

Wang

Cheng

et al. 2020

Symmetry

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With the development of multimedia technology, the secure image retrieval scheme has become a hot research topic. However, how to further improve algorithm performance in the ciphertext needs to be further explored. In this paper, we propose a secure image retrieval scheme based on a deep hash algorithm for index encryption and an improved 4-Dimensional(4-D)hyperchaotic system. The main contributions of this paper are as follows: (1) A novel secure retrieval scheme is proposed to control data transmission. (2) An improved 4-D hyperchaotic system is proposed to preserve privacy. (3) We propose an improved deep pairwise-supervised hashing (DPSH) algorithm and secure kNN to perform index encryption and propose an improved loss function to train the network model. (4) A secure access control scheme is shown, which aims to achieve secure access for users. The experimental results show that the proposed scheme has better retrieval efficiency and better security.

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Section: Index Encryptionmentioning

confidence: 99%

“…In the traditional hash algorithm, SDH [19] was proposed to learn binary hash codes directly. Some hashing algorithms (DPSH [20], DSDH [21], DSEH [22], DDSH [23]) based on supervision and quantization were proposed to evaluate the quality of hash code learning. At the same time, these algorithms were also the baseline of this paper.…”

Section: Resnet18mentioning

confidence: 99%

A Privacy-Protected Image Retrieval Scheme for Fast and Secure Image Search

Wang

Cheng

et al. 2020

Symmetry

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“…To address the inefficiency problem in real-value based retrieval, many hashing methods have been proposed [9,10,11,12]. There have appeared two speaker hashing methods [1,3] for speaker identification and retrieval.…”

Section: Speaker Hashingmentioning

confidence: 99%

Deep Hashing for Speaker Identification and Retrieval

Fan

Jiang

et al. 2019

Interspeech 2019

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Speaker identification and retrieval have been widely used in real applications. To overcome the inefficiency problem caused by real-valued representations, there have appeared some speaker hashing methods for speaker identification and retrieval by learning binary codes as representations. However, these hashing methods are based on i-vector and cannot achieve satisfactory retrieval accuracy as they cannot learn discriminative feature representations. In this paper, we propose a novel deep hashing method, called deep additive margin hashing (DAMH), to improve retrieval performance for speaker identification and retrieval task. Compared with existing speaker hashing methods, DAMH can perform feature learning and binary code learning seamlessly by incorporating these two procedures into an end-to-end architecture. Experimental results on a large-scale audio dataset VoxCeleb2 show that DAMH can outperform existing speaker hashing methods to achieve state-of-the-art performance.

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“…In recent years, deep learning is applied to perceptual hash [21,[24][25][26][27][28][29], which has solved many problems in traditional perceptual hash. Subject-sensitive perceptual hash [29], also known as subject-sensitive hashing, is proposed in this background to realize subject-biased integrity authentication of HRRS image.…”

Section: Introductionmentioning

confidence: 99%

Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

Ding

Su²,

et al. 2021

IEEE Access

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As a special case of perceptual hashing algorithm, subject-sensitive hashing can realize "subject-biased" integrity authentication of high resolution remote sensing (HRRS) images, which overcomes the deficiencies of existing integrity authentication technologies. However, the existing deep neural network for subject-sensitive hashing have disadvantages such as high model complexity and low computational efficiency. In this paper, we propose an efficient and lightweight deep neural network named Semi-U-net to achieve efficient subject-sensitive hashing. The proposed Semi-U-net realizes the lightweight of the network from three aspects: First, considering the general process of perceptual hashing, it adopts a semi-u-shaped structure, which simplify the model structure and prevent the model from extracting too much redundant information to enhance the robustness of the algorithm; Second, the number of model parameters and the computational cost are significantly reduced by using deep separable convolution in the entire asymmetric network; Third, the number of model parameters is further compressed by using the dropout layer several times. The experimental results show that the size of our Semi-U-Net model is only 5.38M, which is only 1/27 of MUM-net and 1/15 of MultiResUnet. The speed of the Semi-U-Net based subject-sensitive hashing algorithm is 88.6 FPS, which is 2.89 times faster than MultiResUnet based algorithm and 2.1 times faster than MUM-net Based Algorithm.

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Deep Discrete Supervised Hashing

Cited by 107 publications

References 33 publications

A Privacy-Protected Image Retrieval Scheme for Fast and Secure Image Search

A Privacy-Protected Image Retrieval Scheme for Fast and Secure Image Search

Deep Hashing for Speaker Identification and Retrieval

Semi-U-Net: A Lightweight Deep Neural Network for Subject-Sensitive Hashing of HRRS Images

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