Blind source computer device identification from recorded VoIP calls for forensic investigation

Jahanirad, Mehdi; Anuar, Nor Badrul; Wahab, Ainuddin Wahid Abdul

doi:10.1016/j.forsciint.2017.01.010

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…The main disadvantage is the need to be familiar with the details of the attack in advance, especially relying on the key information of the attack. (2) In the aspect of electronic evidence analysis in dynamic environment [11][12][13][14], it is based on the research results of electronic evidence analysis technology in static environment, adding time factor to study. Literature [11] aimed at the increasing number of data and equipment to be analyzed in electronic forensics cases, taking time parameters as data correlation factors, and proposed an automatic clustering algorithm based on self-organizing maps, which reduced the influences of noise data on forensics algorithms.…”

Section: Related Workmentioning

confidence: 99%

Research on Data Analysis and Electronic Forensics Algorithm of Telecom Fraud Activity

Zhang

2021

Lecture Notes in Electrical Engineering

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This paper aims to provide effective and complete electronic evidence of telecom fraud activity for public safety agency. We proposed telecom fraud activity computer forensics platform architecture based on the characteristics of telecom operator data. The telephone fraud and SMS fraud were taken as the research object. The electronic evidence table of telecom fraud activities was generated. An electronic evidence analysis algorithm for telecom fraud activities was proposed. Dataset was analyzed from two aspects of user characteristics and fraud types. From the analysis results, it is known that there are more types of telecom fraud that require further analysis and research, in addition to telephone fraud and SMS fraud.

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Section: Related Workmentioning

confidence: 99%

Research on Data Analysis and Electronic Forensics Algorithm of Telecom Fraud Activity

Zhang

2021

Lecture Notes in Electrical Engineering

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“…Aggarwal et al [14] proposed an MFCC feature extraction from an estimated noisy region of the speech. Jahanirad et al [4,15] investigated the use of entropy of Mel-cepstrum coefficients from near-silent segments. Anshan et al [16] used device self-noise estimated from the silent segments.…”

Section: Introductionmentioning

confidence: 99%

Dual Attention Pooling Network for Recording Device Classification Using Neutral and Whispered Speech

Naini

Singhal

Ghosh

2022

ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this work, we proposed a method for recording device classification using the recorded speech signal. With the rapid increase in different mobile and professional recording devices, determining the source device has many applications in forensics and in further improving various speech-based applications. This paper proposes dual and single attention pooling-based convolutional neural networks (CNN) for recording device classification using neutral and whispered speech. Experiments using five recording devices with simultaneous direct recordings from 88 speakers speaking both in neutral and whisper and recordings from 21 mobile devices with simultaneous playback recordings reveal that the proposed dual attention pooling based CNN method performs better than the best baseline scheme. We show that we achieve a better performance in recording device classification with whispered speech recordings than corresponding neutral speech. We also demonstrate the importance of voiced/unvoiced speech and different frequency bands in classifying the recording devices.

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Band Energy Difference for Source Attribution in Audio Forensics

Luo

Korus

Huang

2018

IEEE Trans.Inform.Forensic Secur.

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Blind source computer device identification from recorded VoIP calls for forensic investigation

Cited by 4 publications

References 13 publications

Research on Data Analysis and Electronic Forensics Algorithm of Telecom Fraud Activity

Research on Data Analysis and Electronic Forensics Algorithm of Telecom Fraud Activity

Dual Attention Pooling Network for Recording Device Classification Using Neutral and Whispered Speech

Band Energy Difference for Source Attribution in Audio Forensics

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