Digital Audio Forensics Fundamentals

2020 ◽  
Author(s):  
James Zjalic
Keyword(s):  
Digital Audio ◽  
Audio Forensics

Digital audio forensics

2007 ◽  
Author(s):  
Christian Kraetzer ◽  
Andrea Oermann ◽  
Jana Dittmann ◽  
Andreas Lang
Keyword(s):  
Digital Audio ◽  
Audio Forensics

Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment

2019 ◽  
Vol 11 (2) ◽  
pp. 47-62 ◽  
Author(s):  
Xinchao Huang ◽  
Zihan Liu ◽  
Wei Lu ◽  
Hongmei Liu ◽  
Shijun Xiang
Keyword(s):  
Time Complexity ◽  
State Of The Art ◽  
Digital Audio ◽  
Activity Detection ◽  
Research Focus ◽  
Audio Forensics ◽  
Significant Research ◽  
Audio Data ◽  
Adjacent Segments ◽  
Voice Activity

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.

scholarly journals Blind Detection of Copy-Move Forgery in Digital Audio Forensics

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 12843-12855 ◽  
Author(s):  
Muhammad Imran ◽  
Zulfiqar Ali ◽  
Sheikh Tahir Bakhsh ◽  
Sheeraz Akram
Keyword(s):  
Blind Detection ◽  
Digital Audio ◽  
Audio Forensics

Fast and Effective Copy-Move Detection of Digital Audio Based on Auto Segment

2020 ◽  
pp. 127-142
Author(s):  
Xinchao Huang ◽  
Zihan Liu ◽  
Wei Lu ◽  
Hongmei Liu ◽  
Shijun Xiang
Keyword(s):  
Fourier Transform ◽  
Special Form ◽  
Time Complexity ◽  
Voice Activity Detection ◽  
Digital Audio ◽  
Audio Forensics ◽  
Significant Research ◽  
Audio Data ◽  
Adjacent Segments ◽  
Voice Activity

Detecting digital audio forgeries is a significant research focus in the field of audio forensics. In this article, the authors focus on a special form of digital audio forgery—copy-move—and propose a fast and effective method to detect doctored audios. First, the article segments the input audio data into syllables by voice activity detection and syllable detection. Second, the authors select the points in the frequency domain as feature by applying discrete Fourier transform (DFT) to each audio segment. Furthermore, this article sorts every segment according to the features and gets a sorted list of audio segments. In the end, the article merely compares one segment with some adjacent segments in the sorted list so that the time complexity is decreased. After comparisons with other state of the art methods, the results show that the proposed method can identify the authentication of the input audio and locate the forged position fast and effectively.

scholarly journals Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral Features

2011 ◽  
Vol 62 (4) ◽  
pp. 199-205 ◽  
Author(s):  
Ghulam Muhammad ◽  
Khalid Alghathbar
Keyword(s):  
Recognition Accuracy ◽  
Digital Audio ◽  
Mel Frequency Cepstral Coefficients ◽  
Environmental Sound ◽  
Area Of Interest ◽  
Audio Features ◽  
Audio Forensics ◽  
Cepstral Features ◽  
Environment Recognition ◽  
Audio Files

Environment Recognition for Digital Audio Forensics Using MPEG-7 and MEL Cepstral FeaturesEnvironment recognition from digital audio for forensics application is a growing area of interest. However, compared to other branches of audio forensics, it is a less researched one. Especially less attention has been given to detect environment from files where foreground speech is present, which is a forensics scenario. In this paper, we perform several experiments focusing on the problems of environment recognition from audio particularly for forensics application. Experimental results show that the task is easier when audio files contain only environmental sound than when they contain both foreground speech and background environment. We propose a full set of MPEG-7 audio features combined with mel frequency cepstral coefficients (MFCCs) to improve the accuracy. In the experiments, the proposed approach significantly increases the recognition accuracy of environment sound even in the presence of high amount of foreground human speech.

Statistical Based Audio Forensic on Identical Microphones

2016 ◽  
Vol 6 (5) ◽  
pp. 2211 ◽  
Author(s):  
Fajri Kurniawan ◽  
Mohd. Shafry Mohd. Rahim ◽  
Mohammed S. Khalil ◽  
Muhammad Khurram Khan
Keyword(s):  
Statistical Analysis ◽  
Experimental Results ◽  
Digital Audio ◽  
Robust Method ◽  
Sound Recording ◽  
Audio Recording ◽  
Audio Forensics ◽  
The Difference

<p>Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collected from two microphones of the same model. Experimental results and analysis indicate that the signal of sound recording of identical microphone is not exactly same and the difference is up to 1% - 3%.</p>

scholarly journals Statistical Based Audio Forensic on Identical Microphones

2016 ◽  
Vol 6 (5) ◽  
pp. 2211
Author(s):  
Fajri Kurniawan ◽  
Mohd. Shafry Mohd. Rahim ◽  
Mohammed S. Khalil ◽  
Muhammad Khurram Khan
Keyword(s):  
Statistical Analysis ◽  
Experimental Results ◽  
Digital Audio ◽  
Robust Method ◽  
Sound Recording ◽  
Audio Recording ◽  
Audio Forensics ◽  
The Difference

<p>Microphone forensics has become a challenging field due to the proliferation of recording devices and explosion in video/audio recording. Video or audio recording helps a criminal investigator to analyze the scene and to collect evidences. In this regards, a robust method is required to assure the originality of some recordings. In this paper, we focus on digital audio forensics and study how to identify the microphone model. Defining microphone model will allow the investigators to conclude integrity of some recordings. We perform statistical analysis on the recording that is collected from two microphones of the same model. Experimental results and analysis indicate that the signal of sound recording of identical microphone is not exactly same and the difference is up to 1% - 3%.</p>

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