An algorithm of detecting audio copy-move forgery based on DCT and SVD

Wang, Feiyang; Li, Chen; Lan, Tian

doi:10.1109/icct.2017.8359911

Cited by 21 publications

(7 citation statements)

References 6 publications

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“…In this part, we compared the proposed method with other studies in this eld in the literature to present the e ciency of our method. The results obtained as a result of the proposed method were compared with the results of Lbp method [19], Pitch similarity method [20], Formant method [21], DFT method[16] and DCT-SVD [17]. The proposed method and other studies were coded on the same machine and tested in the audio database created by us.…”

Section: Comparison With Traditional Resultsmentioning

confidence: 99%

“…When the results were examined, the accuracy values of the proposed algorithm in all post-processing operations were 0.99 and above. DCT-SVD [17] Pitch-sim [20] Proposed 20db noise adding 0,5 0.2 0,63 0,6 0,5 0,94 30db noise adding 0,4 0,16 0,6 0,4 0,37 0,93 64kbps compressing 0,3 0,2 0,57 0,3 0,3 0,9…”

Section: Comparison With Traditional Resultsmentioning

confidence: 99%

“…After their method sorted all voiced part according to the features, they compared one voice part with some adjacent parts instead of all parts in the sorted list to reduce the time complexity of algorithm. Wang et al [17] suggested a method using singular value decomposition (SVD) transform to detect audio copy move forgery. They obtained voiced parts from speech recording using a voice activity detection (VAD) method.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Mel spectrogram-based audio forgery detection using CNN

Ustubioglu

Üstübi̇oğlu

Ulutaş

2022

SIViP

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In this time of technology, digital speech can be created and falsi ed by a very diverse of hardware and software technologies. Audio Copy-move forgery is an audio forgery technique that goals to create forged audio by hiding undesirable words or repeating wanted words in the identical speech. Therefore, audio authentication has been a necessary requisition. In this study, an effective approach to spectral images based on audio copy-move forgery detection using Convolutional Neural Networks (CNN) with data augmentation is proposed. There are only a few hand-crafted methods conducted for the detection of audio copy-move forgery. None of the existing works on audio copy-move forgery detection has proposed deep feature learning from speech recording with Mel spectrogram. This is the rst method to employ deep learning with Mel spectrogram of audio for the detection of audio copy-move forgery. The proposed CNN architecture classi es the suspicious Mel spectrogram images into two classes: original and forged. The proposed CNN system is successfully trained on these Mel spectrogram images feature extraction.The proposed algorithm has been tested on our datasets generated from Arabic Speech Corpus and TIMIT speech database. The results show the effectiveness, robustness to post-processing operations, and high accuracy of the proposed approach compared to other studies.

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Section: Comparison With Traditional Resultsmentioning

confidence: 99%

Section: Comparison With Traditional Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Mel spectrogram-based audio forgery detection using CNN

Ustubioglu

Üstübi̇oğlu

Ulutaş

2022

SIViP

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show abstract

“…The similarity of the extracted pitch sequences was calculated using the PCC and the mean difference method (AD). Wang et al (2017) proposed a method based on the Singular Value Decomposition (SVD) transform and the Discrete Cosine Transform (DCT). First, they detected the voiced parts from the audio using a voice activity detection method.…”

Section: Related Workmentioning

confidence: 99%

Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach

Üstübi̇oğlu

Tahaoglu

Ulutaş

et al. 2023

Preprint

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Malicious individuals can modify speech recordings with advanced audio editing software to create forged audio. The most common forgery method, known as audio copy-move forgery, involves copying part of the audio to duplicate or delete a segment. Considering the fact that the speech recording is used as evidence in court, it is of great importance to detect whether the voice recordings are forged or not. To this end, we present an effective and robust method based on BRIEF and OPTICS to detect and locate audio copy-move forgeries. The proposed method uses super-resolution spectrogram images of the input audio to detect forged parts in suspicious audio recordings. For this purpose, key points and their feature descriptors are first extracted from the spectrogram image using the BRIEF method. The Ordering Points To Identify the Clustering Structure method (OPTICS) is used by the approach to match the corresponding descriptors. The proposed approach to eliminate false matches evaluates the correctness of the matches. The method also marks the corresponding forged segments in the audio file based on the location of the keypoints in these clusters. The performance results show that the proposed method has significantly high robustness to post-processing attacks such as noise addition, filtering, and especially compression, as reported in the literature.

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“…(2) Interpolation: By convoluting the signal x u (n) with the low-pass linear interpolation filter h(n), the interpolated signal x c (n) = x(n) * h(n) can be obtained, which can be divided into linear interpolation, spline interpolation, and cubic interpolation according to the different filters [17].…”

Section: Principle Of Resamplingmentioning

confidence: 99%

Exposing Speech Resampling Manipulation by Local Texture Analysis on Spectrogram Images

et al. 2019

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Speech tampering may be aided by the resampling operation. It is significant for speech forensics to effectively detect the resampling; however, there are few studies on speech resampling detection. The purpose of this paper was therefore to provide a new training ideal to detect speech resampling. After resampling, the speech signal changes regularly in the time–frequency domain. In this paper, we theoretically analyzed the corresponding relationship between time domain and frequency domain of the resampled speech. Compared with the original speech, the bandwidth of resampled speech was stretched or compressed. First, the spectrogram was generated by short-time Fourier transform (STFT) from the speech. Then, the local binary pattern (LBP) operator was applied to model the statistical changes in the spectrogram and the LBP histogram was calculated as discriminative features. Finally, a support vector machine (SVM) was applied to classify the developed features to identify whether the speech had undergone the resampling operation. The experimental results show that the proposed method has superior detection performance in different resampling scenarios than some existing methods, and the proposed features are very robust against the commonly used compression post-processing operation. This highlights the promising potential of the proposed method as a speech resampling detection tool in practical forensics applications.

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An algorithm of detecting audio copy-move forgery based on DCT and SVD

Cited by 21 publications

References 6 publications

Mel spectrogram-based audio forgery detection using CNN

Mel spectrogram-based audio forgery detection using CNN

Audio forgery detection and localization with super resolution spectrogram and keypoint based clustering approach

Exposing Speech Resampling Manipulation by Local Texture Analysis on Spectrogram Images

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