scholarly journals Digital Audio Forensics: Microphone and Environment Classification Using Deep Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mustafa Qamhan ◽  
Hamdi Altaheri ◽  
Ali Hamid Meftah ◽  
Ghulam Muhammad ◽  
Yousef A. Alotaibi
Keyword(s):  
Deep Learning ◽  
Digital Audio ◽  
Audio Forensics

Digital audio forensics

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

scholarly journals Application of neuron networks of deep learning for exposures editing of digital phonograms

2020 ◽  
Vol 65 (4) ◽  
pp. 506-512
Author(s):  
V. I. Solovyov ◽  
O. V. Rybalskiy ◽  
V. V. Zhuravel ◽  
V. K. Zheleznyak
Keyword(s):  
Deep Learning ◽  
Binary Classification ◽  
Maximum Size ◽  
Maximum Efficiency ◽  
Correct Classification ◽  
Digital Audio ◽  
Neuron Networks ◽  
Effective System ◽  
Further Development

Possibility of creation of effective system, which is intended for exposure of tracks of editing in digital phonograms and is built on the basis of neuron networks of the deep learning, is experimentally proven. Sense of experiment consisted in research of ability of the systems on the basis of such networks to expose pauses with tracks of editing. The experimental array of data is created in a voice editor from phonograms written on the different apparatus of the digital audio recording (at frequency of discretisation 44,1 kHz). A preselection of pauses was produced from it, having duration from 100 мs to a few seconds. From 1000 selected pauses the array of fragments of pauses is formed in the automatic (computer) mode, from which the arrays of fragments of pauses of different duration are generated by a dimension about 100 000. For forming of array of fragments of pauses with editing, the chosen pauses were divided into casual character parts in arbitrary correlation. Afterwards, the new pauses were created from it with the fixed place of editing. The general array of all fragments of pauses was broken into training and test arrays. The maximum efficiency, achieved on a test array in the process of educating, was determined. In general case this efficiency is determined by the maximum size of probability of correct classification of fragments with editing and fragments without editing. Scientifically reasonable methodology of exposure of signs of editing in digital phonograms is offered on the basis of neuron networks of the deep learning. The conducted experiments showed that the construction of the effective system is possible for the exposure of such tracks. Further development of methodology must be directed to find the ways to increase the probability of correct binary classification of investigated pauses.

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.

scholarly journals Deep Convolutional Neural Network based Image Steganogrpahy Technique for Audio-Image Hiding Algorithm

2020 ◽  
Vol 9 (4) ◽  
pp. 2187-2189
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Digital Image ◽  
Digital Audio ◽  
Least Significant Bit ◽  
Stego Image ◽  
Vast Number ◽  
Learning Techniques ◽  
Network Concept ◽  
Audio Files

Steganography is one expanding filed in the area of Data Security. Steganography has attractive number of application from a vast number of researchers. The most existing technique in steganogarphy is Least Significant Bit (LSB) encoding. Now a day there has been so many new approaches employing with different techniques like deep learning. Those techniques are used to address the problems of steganography. Now a day’s many of the exisiting algorithms are based on the image to data, image to image steganography. In this paper we hide secret audio into the digital image with the help of deep learning techniques. We use a joint deep neural network concept it consist of two sub models. The first model is responsible for hiding digital audio into a digital image. The second model is responsible for returning a digital audio from the stego image. Various vast experiments are conducted with a set of 24K images and also for various sizes of images. From the experiments it can be seen proposed method is performing more effective than the existing methods. The proposed method also concentrates the integrity of the digital image and audio files.

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