An Encrypted Marine Mammal Audio Retrieval Algorithm Based on Biohashing
Marine mammals are crucial subjects for marine audio studies, particularly in the development of retrieval algorithms for encrypted marine mammal audio.To address the issues of low retrieval performance and high content redundancy, and to ensure security of existing marine mammal audio retrieval algorithms, this paper proposes an encrypted marine mammal audio retrieval algorithm based on biohashing. The server terminal first extracts the sub-band CQT energy-entropy ratio of audio to construct biometric dataset. by querying the key and corresponding classified biometric in the key-address index table constructed based on SVM classification, the key-controlled orthogonal matrix and the corresponding classified biometric generate the biosafety template, and the template is quantified to obtain biohashing. Finally, the dual-threshold audio segmentation and position mapping are adopted to perform hash reconstruction to obtain the hash index. Meanwhile, the Logistic-AES encryption method encrypts audio clips to construct the encrypted audio library. Experimental results demonstrate that sub-band CQT energy-entropy ratio has better robustness and discrimination, which results in the higher precision and recall of the retrieval system. And the proposed audio segmentation algorithm reduces the redundant part of audio, improving the efficiency of retrieval system. Meanwhile, the designed encryption method is sensitive to the key and disrupts audio correlation, which ensures the resistance of encrypted audio to statistical attacks. Furthermore, the biosafety template of biohashing has better recoverability, security and diversity.
Authors
- Steven Gao
- Deshun Li (ORCID: https://orcid.org/0000-0003-2026-9288)
- Rongxin Zhu (ORCID: https://orcid.org/0000-0001-5133-0868)
- Yuan Zhang (ORCID: https://orcid.org/0000-0002-4630-0450)
- Qiuling Yang
- D. Qin
Institutions
- Hainan University (CN)
- Hainan Agricultural School (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-10-08
- DOI
- https://doi.org/10.1109/jiot.2025.3619102
- Primary Topic
- Music and Audio Processing
- Type
- article
- Field-Weighted Citation Impact
- 0.00