Forensic authentication method for audio recordings generated by Voice Recorder application on Samsung Galaxy Watch4 series

Park, Nam In; Lim, Seong Ho; Byun, Jun Seok; Kim, Jin Hwan; Lee, Ji Woo; Chun, Chanjun; Kim, Yonggang; Jeon, Oc-Yeub

doi:10.1111/1556-4029.15158

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…They also compared the file structures of the original audio files and the audio files edited using in-built application "voice memo" in iPhones. Again Forensic authentication for audio recordings generated by the voice recorder application in Samsung Galaxy Watch4 series was carried in the paper presented by NI Park et al [14]. The hex data of the audio files provide clear indications of the edition, which may be used to demonstrate the integrity [15,16].…”

Section: Introductionmentioning

confidence: 99%

Metadata and Container Structure Analysis for Audio Authentication

Marathe,

Wayal,

Pawade

et al. 2023

IJEAST

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Audio recordings of crime scenes are frequently utilized as digital evidence due to the universal usage of mobile phones, but they must first be authenticated before being used as evidence in court. The technique of identifying whether an audio recording is real or has been altered or manipulated is known as audio authenticity. In order to investigate and validate audio, this study provides a cutting-edge authentication technique that can distinguish the difference between real and fraudulent audio. Herein, we analyzed audio recording samples from different mobile handsets like iPhone, Samsung, One Plus etc. It is important to note that the proposed method authenticates audio files without regard to the audio content, i.e., without concern to the speaker or the speech. The audio recordings were subjected to manipulation that a human cannot recognize them auditory. The present results show that it is possible to verify the authentication of audio recordings generated through mobile phones or any recorder using metadata and the container structure of the recorded and edited audio file

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Section: Introductionmentioning

confidence: 99%

Metadata and Container Structure Analysis for Audio Authentication

Marathe,

Wayal,

Pawade

et al. 2023

IJEAST

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show abstract

“…When generating audio signals, all devices introduce a certain level of noise, commonly referred to as a machine fingerprint [7,8]. This noise is unique to each device due to variations in software and hardware components [9].…”

Section: Introductionmentioning

confidence: 99%

An End-to-End Transfer Learning Framework of Source Recording Device Identification for Audio Sustainable Security

Wang

Jin²,

Zhang³

et al. 2023

Sustainability

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Source recording device identification poses a significant challenge in the field of Audio Sustainable Security (ASS). Most existing studies on end-to-end identification of digital audio sources follow a two-step process: extracting device-specific features and utilizing them in machine learning or deep learning models for decision-making. However, these approaches often rely on empirically set hyperparameters, limiting their generalization capabilities. To address this limitation, this paper leverages the self-learning ability of deep neural networks and the temporal characteristics of audio data. We propose a novel approach that utilizes the Sinc function for audio preprocessing and combine it with a Deep Neural Network (DNN) to establish a comprehensive end-to-end identification model for digital audio sources. By allowing the parameters of the preprocessing and feature extraction processes to be learned through gradient optimization, we enhance the model’s generalization. To overcome practical challenges such as limited timeliness, small sample sizes, and incremental expression, this paper explores the effectiveness of an end-to-end transfer learning model. Experimental verification demonstrates that the proposed end-to-end transfer learning model achieves both timely and accurate results, even with small sample sizes. Moreover, it avoids the need for retraining the model with a large number of samples due to incremental expression. Our experiments showcase the superiority of our method, achieving an impressive 97.7% accuracy when identifying 141 devices. This outperforms four state-of-the-art methods, demonstrating an absolute accuracy improvement of 4.1%. This research contributes to the field of ASS and provides valuable insights for future studies in audio source identification and related applications of information security, digital forensics, and copyright protection.

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Speech coding techniques and challenges: a comprehensive literature survey

Anees,

2023

Multimed Tools Appl

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Forensic authentication method for audio recordings generated by Voice Recorder application on Samsung Galaxy Watch4 series

Cited by 5 publications

References 12 publications

Metadata and Container Structure Analysis for Audio Authentication

Metadata and Container Structure Analysis for Audio Authentication

An End-to-End Transfer Learning Framework of Source Recording Device Identification for Audio Sustainable Security

Speech coding techniques and challenges: a comprehensive literature survey

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