Copy and Move Detection in Audio Recordings using Dynamic Time Warping Algorithm

Mannepalli*, Kasiprasad; Krishna, Pelluri vamsi; Krishna, Kovi vamsi; Krishna, Kodali rama

doi:10.35940/ijitee.b6678.129219

Cited by 9 publications

(3 citation statements)

References 17 publications

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“…We designate a specific feature f(x, y) as i and quantify the correlation between i and all other features f(x, y) to construct a score vector S[i]. This vector contains a total of 128 correlations between features, including the correlation of i with itself and 127 other features, as shown in Equation (3).…”

Section: Correlation Detection Modulementioning

confidence: 99%

“…The existing passive evidence-gathering methods typically involve three steps: The first step is to segment the audio into word or phrase segments, remove silent segments, and then extract various features from each segment. For audio segmentation, more researchers [2][3][4][5][6][7][8][9][10] use Voice Activity Detection (VAD) to divide the audio into silent and audio segments and [11,12] use Normalized Low-Frequency Energy Ratio (NLFER) to divide the audio into silent and audio segments and [13] to divide the audio into equal time periods. Then, various features are extracted from the segmented audio segments, such as singular feature vector [2], Mel scale frequency cepstral coefficient (MFCC) [3,7], fundamental sequence [4], discrete Fourier transform (DFT) coefficient [5][6][7], gamma filter [7], histogram [8,9], color autocorrelation map [10], tone similarity [11], tone sequence [12], format sequence [12], etc.…”

Section: Introductionmentioning

confidence: 99%

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An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis

Zhao,

Zhang,

Wang

et al. 2024

Applied Sciences

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In audio copy-move forgery forensics, existing traditional methods typically first segment audio into voiced and silent segments, then compute the similarity between voiced segments to detect and locate forged segments. However, audio collected in noisy environments is difficult to segment and manually set, and heuristic similarity thresholds lack robustness. Existing deep learning methods extract features from audio and then use neural networks for binary classification, lacking the ability to locate forged segments. Therefore, for locating audio copy-move forgery segments, we have improved deep learning methods and proposed a robust localization model by CNN-based spectral analysis. In the localization model, the Feature Extraction Module extracts deep features from Mel-spectrograms, while the Correlation Detection Module automatically decides on the correlation between these deep features. Finally, the Mask Decoding Module visually locates the forged segments. Experimental results show that compared to existing methods, the localization model improves the detection accuracy of audio copy-move forgery by 3.0–6.8%and improves the average detection accuracy of forged audio with post-processing attacks such as noise, filtering, resampling, and MP3 compression by over 7.0%.

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Section: Correlation Detection Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis

Zhao,

Zhang,

Wang

et al. 2024

Applied Sciences

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“…The dynamic time warping (DTW) method was used to calculate the similarity of these features. Mannepalli et al (2019) obtained the MFCCs features of each voice part and also compared the similarity of feature vectors by the DTW distance.…”

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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