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Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part II

Research Article

A PUF Based Audio Fingerprint Based for Device Authentication and Tamper Location

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-56583-0_5,
        author={Zhi Lu and Haochen Dou and Songfeng Lu and Xueming Tang and Junjun Wu and Samir Mohammed Umran},
        title={A PUF Based Audio Fingerprint Based for Device Authentication and Tamper Location},
        proceedings={Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part II},
        proceedings_a={ICDF2C PART 2},
        year={2024},
        month={4},
        keywords={Physical Unclonable Function Voice Recoder Identification Digital Audio Forensics Landmark},
        doi={10.1007/978-3-031-56583-0_5}
    }
    
  • Zhi Lu
    Haochen Dou
    Songfeng Lu
    Xueming Tang
    Junjun Wu
    Samir Mohammed Umran
    Year: 2024
    A PUF Based Audio Fingerprint Based for Device Authentication and Tamper Location
    ICDF2C PART 2
    Springer
    DOI: 10.1007/978-3-031-56583-0_5
Zhi Lu1, Haochen Dou1, Songfeng Lu1, Xueming Tang1,*, Junjun Wu1, Samir Mohammed Umran1
  • 1: Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology
*Contact email: xmtang@hust.edu.cn

Abstract

As bioinformation authentication gains prominence, the significance of audio data in industries such as speech recognition intensifies, with audio storage becoming a pivotal concern for data protection. Existing audio tampering solutions fail to identify the producing device. This paper introduces an innovative method employing physical unclonable function (PUF) and audio features for identifying recording equipment and detecting tampered areas in judicial authentication within the Industrial Internet-of-Things (IIoT). The method comprises two components: the recording device, which generates an audio fingerprint using audio features and a PUF-determined random number seed, and the server, which registers, analyzes, and verifies the fingerprint. The unique, tamper-resistant PUF response is generated only when a server-provided challenge is initiated. The proposed audio fingerprint, evaluated using the Carioca 1 database and NXP LPC54S018-EVK-provided PUF functionality, enables varying tamper area identification accuracy and achieves 100% original device identification, resisting replay, cloning, and brute force attacks.

Keywords
Physical Unclonable Function Voice Recoder Identification Digital Audio Forensics Landmark
Published
2024-04-03
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-56583-0_5
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