Supervised audio tampering detection using an autoregressive model

Lin, Xiaodan; Kang, Xiangui

doi:10.1109/icassp.2017.7952535

Cited by 38 publications

(25 citation statements)

References 13 publications

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“…During the last decade, many audio splicing detection and localization methods based on ENF signal and local noise levels have been proposed. Lin and Kang [8] proposed a wavelet-filtered ENF signal to highlight the abnormal ENF variations and employed autoregressive coefficients to train the classifier under a supervised-learning framework. Mao et al [9] utilized the multiple ENF features as input eigenvectors to the convolutional neural networks for detecting spliced audio.…”

Section: Audio Splicing Detentionmentioning

confidence: 99%

“…However, when the signal-to-noise ratio between the spliced segments is close or even the same, the performance of the noise levels based audio splicing detection methods will decrease sharply. In addition, based on the fact that inserting an audio segment into another audio recording leads to anomalous variations of the electric network frequency (ENF) signal, several kinds of research [7][8][9] have shown that it is an efficient way to detect spliced audio via the analysis of ENF signal. Whereas due to legal restrictions, it is difficult to obtain concurrent reference datasets of power systems, which makes the ENF based audio splicing detection methods difficult to implement [10].…”

Section: Introductionmentioning

confidence: 99%

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ASLNet: An Encoder-Decoder Architecture for Audio Splicing Detection and Localization

Zhang

Zhao

2022

Security and Communication Networks

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Audio splicing means inserting an audio segment into another audio, which presents a great challenge to audio forensics. In this paper, a novel audio splicing detection and localization method based on an encoder-decoder architecture (ASLNet) is proposed. Firstly, an audio clip is divided into several small audio segments according to the size of the smallest localization region L slr , and the acoustic feature matrix and corresponding binary ground truth mask are created from each audio segment. Then, we concatenate acoustic feature matrices from all segments of an audio clip into an acoustic feature matrix and send it to a fully convolutional network (FCN) based encoder-decoder architecture which consists of a series of convolutional, pooling and transposed convolutional layers to get a binary output mask. Next, the binary output mask is divided into small segments according to the L slr , and the ratio ρ of the number of elements equal to one to the number of all elements in a small segment is calculated. Finally, we compare ρ with the predetermined threshold T to determine whether the corresponding audio segment is spliced. We evaluate the effectiveness of the proposed ASLNet on four datasets produced from publicly available speech corpus. Extensive experiments show that the best detection accuracy of ASLNet for the intradatabase and cross-database evaluation can achieve 0.9965 and 0.9740 receptively, which outperforms the state-of-the-art method.

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Section: Audio Splicing Detentionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ASLNet: An Encoder-Decoder Architecture for Audio Splicing Detection and Localization

Zhang

Zhao

2022

Security and Communication Networks

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“…In our proposed system, training the system with forged audio is not required. When detecting splicing, the systems developed in [11], [24],…”

Section: Summary and Comparisonmentioning

confidence: 99%

Detection of impostor and tampered segments in audio by using an intelligent system

Mubeen

Afzal

Ali

et al. 2021

Computers & Electrical Engineering

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“…A different approach was explained by Lin & Kang (Lin & Kang, 2017), the detection of audio tampering was done by developing an effective detector for multiple types of audio forgeries. Based on the fact that tampering with an audio leads to anomalous variations of the underlying ENF signal, a wavelet filter to the extracted ENF was applied, followed by an autoregressive modeling of the detail ENF signal.…”

Section: Literature Reviewmentioning

confidence: 99%

Verifying the Audio Evidence to Assist Forensic Investigation

Fayyad-Kazan¹,

Hejase²,

Moukadem³

et al. 2021

CIS

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Audio forensics is a field in forensics that is used to authenticate, enhance, and analyze audio files to aid in solving different crime investigations. Audio as a forensic evidence must be enhanced and analyzed to be admissible in courts of law. But more importantly, it must be authenticated in order to prove that it is authentic and no manipulations were done to it. In this paper, an overview on audio forensics is presented, previous related work to this topic is shown, and methodologies for audio enhancement and authentication are explained along with audio tampering ways and signatures presentation.

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Supervised audio tampering detection using an autoregressive model

Cited by 38 publications

References 13 publications

ASLNet: An Encoder-Decoder Architecture for Audio Splicing Detection and Localization

ASLNet: An Encoder-Decoder Architecture for Audio Splicing Detection and Localization

Detection of impostor and tampered segments in audio by using an intelligent system

Verifying the Audio Evidence to Assist Forensic Investigation

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