A retrieval method for encrypted speech based on improved power normalized cepstrum coefficients and perceptual hashing

Zhang, Qiu-yu; Bai, Jian; Xu, Fu-jiu

doi:10.1007/s11042-022-12560-5

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Wang et al 34 used discrete wavelet transform (DWT) for hash feature extraction and finally for covert channel detection. Zhang et al 35 proposed an algorithm for encrypted speech retrieval uses DWT and principal component analysis (PCA) for feature extraction. Maamar et al 36 used the DCT and discrete sine transform (DST) of the differential block luminance mean (DBLM) features to compute the hash sequence for replay attack detection (RAD).…”

Section: Related Workmentioning

confidence: 99%

A generalized detection framework for covert timing channels based on perceptual hashing

Zhuang,

Chen,

Tian

2024

Trans Emerging Tel Tech

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Network covert channels use network resources to transmit data covertly, and their existence will seriously threaten network security. Therefore, an effective method is needed to prevent and detect them. Current network covert timing channel detection methods often incorporate machine learning methods in order to achieve generalized detection, but they consume a large amount of computational resources. In this paper, we propose a generalized detection framework for covert channels based on perceptual hashing without relying on machine learning methods. And we propose a one‐dimensional data feature descriptor for feature extraction of perceptual hash for the data characteristics of covert timing channels. We first generate the hash sequence of the corresponding channel to get the average hash, which is used for comparison in the test phase. The experimental results show that the feature descriptor can capture the feature differences of one‐dimensional data well. When compared to machine learning methods, this perceptual hashing algorithms enable faster traffic detection. Meanwhile, our method is able to detect the effectiveness with the smallest coverage window compared with the latest solutions. Moreover, it exhibits robustness in jitter network environment.

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Section: Related Workmentioning

confidence: 99%

A generalized detection framework for covert timing channels based on perceptual hashing

Zhuang,

Chen,

Tian

2024

Trans Emerging Tel Tech

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show abstract

“…In order to obtain more results in the direction of encrypted speech retrieval, Li et al [23] proposed an improved segmented matching method to solve the problem that dynamic time warping (DTW) cannot be used for long speech signal retrieval, but the retrieval accuracy will be affected by inaccurate truncation. Zhang et al [24] proposed an encrypted speech retrieval method based on the cepstrum coefficient and perceptual hash, which uses discrete wavelet transform and first-order difference coefficient to extract features, improving the retrieval accuracy but with high complexity. Li et al [25] proposed a low-dimensional audio fingerprint extraction method based on local linear embedding and an efficient hierarchical retrieval method, which uses the hash value of a single-frame audio fingerprint to construct a hash table, which reduces the retrieval complexity, but the algorithm is less robust.…”

Section: Related Workmentioning

confidence: 99%

“…Existing encrypted speech retrieval schemes utilize various easily distinguishable speech features [22], such as perceptual hashing, biological hashing, deep hashing, audio fingerprints, etc. [23][24][25][26][27][28][29][30][31][32], which have relatively stable retrieval efficiency and accuracy. However, speech features' robustness and retrieval accuracy still need to be improved.…”

Section: Introductionmentioning

confidence: 99%

Multi-User and multi-keyword verifiable encrypted speech retrieval method based on blockchain

Li,

Zhang,

Huang

et al. 2023

Preprint

Self Cite

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Aiming at the existing encrypted speech retrieval schemes that do not support multi-user multi-keyword retrieval, low retrieval efficiency and accuracy, lack of verifiability of encrypted speech retrieval results, and user fairness, a multi-user and multi-keyword verifiable encrypted speech retrieval method based on blockchain was proposed. Firstly, use the advanced encryption standard-128 (AES-128) to encrypt the speech and upload it to the cloud server. Secondly, the Mel-frequency cepstral coefficient (MFCC) and Filter bank (Fbank) speech features are extracted and fused into new MFCC-Fbank features, which are input into the designed CNN-BiGRU model for training. These features are used to extract more robust deep features to construct hash codes and are used as searchable encrypted keywords. Finally, use smart contracts to store encrypted speech hash codes and corresponding index values and manage multi-user keys through trusted institutions. When retrieval, smart contracts are used as trusted third parties for retrieval work, and the designed adaptive homomorphic Message Authentication Code (MAC) algorithm is used to verify the correctness of the retrieval results, ensuring fairness in verification and data privacy. The experimental results show that the proposed method effectively prevents user privacy leakage and improves encrypted speech retrieval accuracy and data security under multi-user and multi-keyword conditions.

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Efficient encrypted speech retrieval based on hadoop cluster under SW CPU

Li,

Jing,

Zhang

et al. 2024

Multimed Tools Appl

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A retrieval method for encrypted speech based on improved power normalized cepstrum coefficients and perceptual hashing

Cited by 4 publications

References 23 publications

A generalized detection framework for covert timing channels based on perceptual hashing

A generalized detection framework for covert timing channels based on perceptual hashing

Multi-User and multi-keyword verifiable encrypted speech retrieval method based on blockchain

Efficient encrypted speech retrieval based on hadoop cluster under SW CPU

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