A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations
The rapid advancement of digital environments has led to an increase in multimedia forgery, particularly in the realm of audio, which leads to significant threats to the reliability of digital evidence. This paper presents a novel method to detect audio copy-move forgery, a type of manipulation wher...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856006/ |
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| author | Beste Ustubioglu Gul Tahaoglu Arda Ustubioglu Guzin Ulutas Muhammed Kilic |
| author_facet | Beste Ustubioglu Gul Tahaoglu Arda Ustubioglu Guzin Ulutas Muhammed Kilic |
| author_sort | Beste Ustubioglu |
| collection | DOAJ |
| description | The rapid advancement of digital environments has led to an increase in multimedia forgery, particularly in the realm of audio, which leads to significant threats to the reliability of digital evidence. This paper presents a novel method to detect audio copy-move forgery, a type of manipulation where segments of an audio file are duplicated and moved to different locations within the same file. The proposed method consists of two main stages. In the first stage, the frequency range containing the forged segments is identified by extracting high-resolution spectrograms from the audio and matching keypoints within the spectrogram images to detect duplicated segments. The frequency range of the sub-spectrogram images with the highest match density is considered the location of the repeated segments. A swarm-based optimization approach is used to adaptively determine this dense region. The audio is then a bandpass filtered using the identified frequency range, and the second stage begins. In this stage, the filtered audio is represented as a graph using the proposed spiral pattern information extraction method. Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. It provides a robust solution for verifying audio authenticity, even under various additional attack scenarios, and shows potential for generalization. |
| format | Article |
| id | doaj-art-23f5f310337b4dc9bd56725c1c398947 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-23f5f310337b4dc9bd56725c1c3989472025-02-06T00:00:30ZengIEEEIEEE Access2169-35362025-01-0113220292205410.1109/ACCESS.2025.353584010856006A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based RepresentationsBeste Ustubioglu0https://orcid.org/0000-0001-7451-0634Gul Tahaoglu1https://orcid.org/0000-0002-8828-5674Arda Ustubioglu2https://orcid.org/0000-0002-8656-8697Guzin Ulutas3https://orcid.org/0000-0001-5729-6613Muhammed Kilic4Computer Engineering Department, Karadeniz Technical University, Trabzon, TürkiyeComputer Engineering Department, Karadeniz Technical University, Trabzon, TürkiyeDepartment of Management Information Systems, Trabzon University, Trabzon, TürkiyeComputer Engineering Department, Karadeniz Technical University, Trabzon, TürkiyeComputer Engineering Department, Karadeniz Technical University, Trabzon, TürkiyeThe rapid advancement of digital environments has led to an increase in multimedia forgery, particularly in the realm of audio, which leads to significant threats to the reliability of digital evidence. This paper presents a novel method to detect audio copy-move forgery, a type of manipulation where segments of an audio file are duplicated and moved to different locations within the same file. The proposed method consists of two main stages. In the first stage, the frequency range containing the forged segments is identified by extracting high-resolution spectrograms from the audio and matching keypoints within the spectrogram images to detect duplicated segments. The frequency range of the sub-spectrogram images with the highest match density is considered the location of the repeated segments. A swarm-based optimization approach is used to adaptively determine this dense region. The audio is then a bandpass filtered using the identified frequency range, and the second stage begins. In this stage, the filtered audio is represented as a graph using the proposed spiral pattern information extraction method. Graph coloring algorithms are applied to convert the graph into a visual representation, which is then input into a specially designed Convolutional Neural Network (CNN) model for classification. The trained model was evaluated using five different datasets, demonstrating that this approach generally outperforms existing methods in terms of detection accuracy. It provides a robust solution for verifying audio authenticity, even under various additional attack scenarios, and shows potential for generalization.https://ieeexplore.ieee.org/document/10856006/Audio forgeryaudio copy-move forgery detectiongraph image representation of audio |
| spellingShingle | Beste Ustubioglu Gul Tahaoglu Arda Ustubioglu Guzin Ulutas Muhammed Kilic A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations IEEE Access Audio forgery audio copy-move forgery detection graph image representation of audio |
| title | A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations |
| title_full | A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations |
| title_fullStr | A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations |
| title_full_unstemmed | A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations |
| title_short | A Novel Audio Copy Move Forgery Detection Method With Classification of Graph-Based Representations |
| title_sort | novel audio copy move forgery detection method with classification of graph based representations |
| topic | Audio forgery audio copy-move forgery detection graph image representation of audio |
| url | https://ieeexplore.ieee.org/document/10856006/ |
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