Araştırma Makalesi
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Görsel kelime tabanlı ses sahteciliği tespit yöntemi

Yıl 2024, Cilt: 13 Sayı: 1, 350 - 358, 15.01.2024
https://doi.org/10.28948/ngumuh.1363316

Öz

Ses kayıtlarının adli olaylarda delil olarak kullanılması halinde, bu kayıtlarının içeriğinin değiştirilmesi suç teşkil etmektedir. Ses kopyala-yapıştır sahteciliği, konuşmanın içeriğini değiştirmek amacıyla yapılan sahteciliklerden en yaygın olanıdır. Bu sahtecilik, sesteki bir kelime ya da kelime grubunun kopyalanıp, aynı sesin içinde herhangi bir konuma yapıştırılmasıyla gerçekleştirilmektedir. Bu çalışmada ses kopyala-yapıştır sahteciliğini tespit etmek için görsel kelimelere dayalı sağlam ve yeni bir yöntem önerilmektedir. Önerilen yöntem, şüpheli ses dosyasındaki sahtecilik ipuçlarını tespit etmek için sesten elde edilen kelimelerin Mel-Spectogram görüntülerini kullanır. Bu amaçla ses dosyası öncelikle perde bazlı ses aktivite algılama (Voice Activity Detection-VAD) yöntemi kullanılarak kelimelere ayrılır. Daha sonra her kelime Mel Spectogram görüntüsüne dönüştürülür. Spectogram görüntüleri arasındaki benzerliği hesaplamak için yapısal farklılık (Structural Difference-DSSIM) kullanılır. Kelime görüntüleri arasındaki DSSIM değerlerine göre sahte kelimeler işaretlenir. Deneysel sonuçlar, önerilen yöntemin diğer çalışmalara kıyasla son işlem operasyonlarına karşı önemli ölçüde yüksek dayanıklılığa sahip olduğunu ve daha yüksek doğruluk değerini verdiğini göstermektedir.

Kaynakça

  • S. Keser, Ö. N. Gerek, E. Seke, M. B. Gülmezoğlu, A subspace based progressive coding method for speech compression. Speech Communication, 94, 50-61, 2017. https://doi.org/10.1016/j.specom.2017.09.002
  • S. Keser, R. Edizkan, Phonem-based isolated Turkish word recognition with subspace classifier. In 2009 IEEE 17th Signal Processing and Communications Applications Conference, pp. 93-96, Antalya, Turkey 2009.
  • O. F. Çıplak, S. Kevser, Gerçek zamanlı ses tanıma ile robot kolu kontrolü. Avrupa Bilim ve Teknoloji Dergisi, 31, 34-39, 2021. https://doi.org/10.31590/ejosat.969608
  • J. N. Xiao, Y. Z Jia, E. D Fu., Z. Huang, Y. Li, & S. P. Shi, Audio authenticity: Duplicated audio segment detection in waveform audio file. Journal of Shanghai Jiaotong University (Science), 19, 392-397, 2014. https://doi.org/10.1007/s12204-014-1515-5
  • Z. Su, M. Li, G. Zhang, Q. Wu, & Y. Wang, Robust audio copy-move forgery detection on short forged slices using sliding window. Journal of Information Security and Applications, 75, 103507, 2023. https://doi.org/10.1016/j.jisa.2023.103507
  • Z. Su, M. Li, G. Zhang, Q. Wu, M. Li, W. Zhang & X. Yao, Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM. IEEE Transactions on Dependable and Secure Computing, 2022. https://doi.org/10.1109/TDSC.2022.3215280
  • F. Wang, C. Li, L. Tian, An algorithm of detecting audio copy-move forgery based on DCT and SVD. In: 2017 IEEE 17th International Conference on Communication Technology (ICCT). IEEE, pp. 1652–1657, Chengdu, China, 2017.
  • Q. Yan, R. Yang, J.Huang, Copy-move detection of audio recording with pitch similarity. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1782–1786, Brisbane, Australia, 2015.
  • Z. Xie, W. Lu, X. Liu, Y. Xue, Y. Yeung, Copy-move detection of digital audio based on multifeature decision. Journal of Information Security and Applications, 43, 37-46, 2018. https://doi.org/10.1016/j.jisa.2018.10.003
  • M. Imran, Z. Ali, S.T. Bakhsh, S. Akram, Blind detection of copy-move forgery in digital audio forensics. IEEE Access 5, 12843–12855, 2017. https://doi.org/10.1109/ACCESS.2017.2717842
  • N.T. Anh, H.T.T. Hang, G. Chen, One approach in the time domain in detecting copy-move of speech recordings with the similar magnitude. International Journal of Engineering and Applied Sciences (IJEAS), 6(4), 9–11, 2019. https://dx.doi.org/10.31873/IJEAS/6.4.2019.05
  • K. Mannepalli, P. Krishna, K. Krishna, Copy and move detection in audio recordings using dynamic time warping algorithm. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(2), 2244–2249, 2019. http://doi.org/10.35940/ijitee.B6678.129219
  • B. Ustubioglu, B. Kucukugurlu, G. Ulutas, Robust copy-move detection in digital audio forensics based on pitch and modifed discrete cosine transform. Multimedia Tools and Applications, 81, 27149–27185, 2022. https://doi.org/10.1007/s11042-022-13035-3
  • X. Huang, Zi Liu, W. Lu, H. Liu, S. Xiang, Fast and effective copy-move detection of digital audio based on auto segment. In: Digital forensics and forensic investigations: breakthroughs in research and practice. IGI Global, pp. 127–142, 2020.
  • Q. Yan, R. Yang, J. Huang, Robust copy–move detection of speech recording using similarities of pitch and formant. IEEE Transactions on Information Forensics and Security, 14(9), 2331–2341, 2019. https://doi.org/10.1109/TIFS.2019.2895965
  • A. Ustubioglu, B. Ustubioglu, G. Ulutas, Mel spectrogram-based audio forgery detection using CNN. Signal, Image and Video Processing, 17, 2211 - 2219, 2022. https://doi.org/10.1007/s11760-022-02436-4
  • B. Ustubioglu, G. Tahaoglu, G. Ulutas, Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram. Expert Systems with Applications 213, 118963, 2023. https://doi.org/10.1016/j.eswa.2022.118963
  • B. Ustubioglu, G. Tahaoglu, G. Ulutas, A. Ustubioglu & M. Kilic, Audio forgery detection and localization with super-resolution spectrogram and keypoint-based clustering approach. The Journal of Supercomputing, 80, 486-518, 2023. https://doi.org/10.1007/s11227-023-05504-9
  • A. Stephen, H. Hu,A spectral/temporal method for robust fundamental frequency tracking,The Journal of the Acoustical Society of America, 123:6, pp:4559-4571,2008. https://doi.org/10.1121/1.2916590
  • Z. Wang, A.C, Bovik Sheikh, H.R., Simoncelli, E.P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 13:4, 600–612, 2004. https://doi.org/10.1109/TIP.2003.819861
  • TIMIT Acoustic-Phonetic Continuous Speech Corpus, https://catalog.ldc.upenn.edu/LDC93s1

Audio forgery detection method based on visual word

Yıl 2024, Cilt: 13 Sayı: 1, 350 - 358, 15.01.2024
https://doi.org/10.28948/ngumuh.1363316

Öz

Changing the content of these recordings constitutes a crime if speech recordings are used as evidence in judicial cases. Audio copy-move forgery is the most common forgery made to change the content of the conversation. This forgery is carried out by copying a word or group of words in the speech and pasting it to any position within the same speech. In this study, a robust new method based on visual words is proposed to detect audio copy-move forgery. The proposed method uses Mel-Spectogram images of words extracted from the audio to detect forgery clues in the suspicious audio file. For this purpose, the audio file is first separated into words using the pitch-based VAD method. Each word is then converted into a Mel Spectogram image. DSSIM is used to calculate the similarity between spectrogram images. Forgery segments are marked according to the DSSIM values between word images. Experimental results show that the proposed method has significantly higher robustness to post-processing operations and yields higher accuracy compared to other works.

Kaynakça

  • S. Keser, Ö. N. Gerek, E. Seke, M. B. Gülmezoğlu, A subspace based progressive coding method for speech compression. Speech Communication, 94, 50-61, 2017. https://doi.org/10.1016/j.specom.2017.09.002
  • S. Keser, R. Edizkan, Phonem-based isolated Turkish word recognition with subspace classifier. In 2009 IEEE 17th Signal Processing and Communications Applications Conference, pp. 93-96, Antalya, Turkey 2009.
  • O. F. Çıplak, S. Kevser, Gerçek zamanlı ses tanıma ile robot kolu kontrolü. Avrupa Bilim ve Teknoloji Dergisi, 31, 34-39, 2021. https://doi.org/10.31590/ejosat.969608
  • J. N. Xiao, Y. Z Jia, E. D Fu., Z. Huang, Y. Li, & S. P. Shi, Audio authenticity: Duplicated audio segment detection in waveform audio file. Journal of Shanghai Jiaotong University (Science), 19, 392-397, 2014. https://doi.org/10.1007/s12204-014-1515-5
  • Z. Su, M. Li, G. Zhang, Q. Wu, & Y. Wang, Robust audio copy-move forgery detection on short forged slices using sliding window. Journal of Information Security and Applications, 75, 103507, 2023. https://doi.org/10.1016/j.jisa.2023.103507
  • Z. Su, M. Li, G. Zhang, Q. Wu, M. Li, W. Zhang & X. Yao, Robust Audio Copy-Move Forgery Detection Using Constant Q Spectral Sketches and GA-SVM. IEEE Transactions on Dependable and Secure Computing, 2022. https://doi.org/10.1109/TDSC.2022.3215280
  • F. Wang, C. Li, L. Tian, An algorithm of detecting audio copy-move forgery based on DCT and SVD. In: 2017 IEEE 17th International Conference on Communication Technology (ICCT). IEEE, pp. 1652–1657, Chengdu, China, 2017.
  • Q. Yan, R. Yang, J.Huang, Copy-move detection of audio recording with pitch similarity. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, pp. 1782–1786, Brisbane, Australia, 2015.
  • Z. Xie, W. Lu, X. Liu, Y. Xue, Y. Yeung, Copy-move detection of digital audio based on multifeature decision. Journal of Information Security and Applications, 43, 37-46, 2018. https://doi.org/10.1016/j.jisa.2018.10.003
  • M. Imran, Z. Ali, S.T. Bakhsh, S. Akram, Blind detection of copy-move forgery in digital audio forensics. IEEE Access 5, 12843–12855, 2017. https://doi.org/10.1109/ACCESS.2017.2717842
  • N.T. Anh, H.T.T. Hang, G. Chen, One approach in the time domain in detecting copy-move of speech recordings with the similar magnitude. International Journal of Engineering and Applied Sciences (IJEAS), 6(4), 9–11, 2019. https://dx.doi.org/10.31873/IJEAS/6.4.2019.05
  • K. Mannepalli, P. Krishna, K. Krishna, Copy and move detection in audio recordings using dynamic time warping algorithm. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 9(2), 2244–2249, 2019. http://doi.org/10.35940/ijitee.B6678.129219
  • B. Ustubioglu, B. Kucukugurlu, G. Ulutas, Robust copy-move detection in digital audio forensics based on pitch and modifed discrete cosine transform. Multimedia Tools and Applications, 81, 27149–27185, 2022. https://doi.org/10.1007/s11042-022-13035-3
  • X. Huang, Zi Liu, W. Lu, H. Liu, S. Xiang, Fast and effective copy-move detection of digital audio based on auto segment. In: Digital forensics and forensic investigations: breakthroughs in research and practice. IGI Global, pp. 127–142, 2020.
  • Q. Yan, R. Yang, J. Huang, Robust copy–move detection of speech recording using similarities of pitch and formant. IEEE Transactions on Information Forensics and Security, 14(9), 2331–2341, 2019. https://doi.org/10.1109/TIFS.2019.2895965
  • A. Ustubioglu, B. Ustubioglu, G. Ulutas, Mel spectrogram-based audio forgery detection using CNN. Signal, Image and Video Processing, 17, 2211 - 2219, 2022. https://doi.org/10.1007/s11760-022-02436-4
  • B. Ustubioglu, G. Tahaoglu, G. Ulutas, Detection of audio copy-move-forgery with novel feature matching on Mel spectrogram. Expert Systems with Applications 213, 118963, 2023. https://doi.org/10.1016/j.eswa.2022.118963
  • B. Ustubioglu, G. Tahaoglu, G. Ulutas, A. Ustubioglu & M. Kilic, Audio forgery detection and localization with super-resolution spectrogram and keypoint-based clustering approach. The Journal of Supercomputing, 80, 486-518, 2023. https://doi.org/10.1007/s11227-023-05504-9
  • A. Stephen, H. Hu,A spectral/temporal method for robust fundamental frequency tracking,The Journal of the Acoustical Society of America, 123:6, pp:4559-4571,2008. https://doi.org/10.1121/1.2916590
  • Z. Wang, A.C, Bovik Sheikh, H.R., Simoncelli, E.P. Image quality assessment: From error visibility to structural similarity. IEEE Transactions on Image Processing. 13:4, 600–612, 2004. https://doi.org/10.1109/TIP.2003.819861
  • TIMIT Acoustic-Phonetic Continuous Speech Corpus, https://catalog.ldc.upenn.edu/LDC93s1
Toplam 21 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Adli Bilişim
Bölüm Araştırma Makaleleri
Yazarlar

Beste Üstübioğlu 0000-0001-7451-0634

Arda Üstübioğlu 0000-0002-8656-8697

Erken Görünüm Tarihi 11 Ocak 2024
Yayımlanma Tarihi 15 Ocak 2024
Gönderilme Tarihi 19 Eylül 2023
Kabul Tarihi 11 Aralık 2023
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 1

Kaynak Göster

APA Üstübioğlu, B., & Üstübioğlu, A. (2024). Görsel kelime tabanlı ses sahteciliği tespit yöntemi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 13(1), 350-358. https://doi.org/10.28948/ngumuh.1363316
AMA Üstübioğlu B, Üstübioğlu A. Görsel kelime tabanlı ses sahteciliği tespit yöntemi. NÖHÜ Müh. Bilim. Derg. Ocak 2024;13(1):350-358. doi:10.28948/ngumuh.1363316
Chicago Üstübioğlu, Beste, ve Arda Üstübioğlu. “Görsel Kelime Tabanlı Ses sahteciliği Tespit yöntemi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13, sy. 1 (Ocak 2024): 350-58. https://doi.org/10.28948/ngumuh.1363316.
EndNote Üstübioğlu B, Üstübioğlu A (01 Ocak 2024) Görsel kelime tabanlı ses sahteciliği tespit yöntemi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13 1 350–358.
IEEE B. Üstübioğlu ve A. Üstübioğlu, “Görsel kelime tabanlı ses sahteciliği tespit yöntemi”, NÖHÜ Müh. Bilim. Derg., c. 13, sy. 1, ss. 350–358, 2024, doi: 10.28948/ngumuh.1363316.
ISNAD Üstübioğlu, Beste - Üstübioğlu, Arda. “Görsel Kelime Tabanlı Ses sahteciliği Tespit yöntemi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi 13/1 (Ocak 2024), 350-358. https://doi.org/10.28948/ngumuh.1363316.
JAMA Üstübioğlu B, Üstübioğlu A. Görsel kelime tabanlı ses sahteciliği tespit yöntemi. NÖHÜ Müh. Bilim. Derg. 2024;13:350–358.
MLA Üstübioğlu, Beste ve Arda Üstübioğlu. “Görsel Kelime Tabanlı Ses sahteciliği Tespit yöntemi”. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, c. 13, sy. 1, 2024, ss. 350-8, doi:10.28948/ngumuh.1363316.
Vancouver Üstübioğlu B, Üstübioğlu A. Görsel kelime tabanlı ses sahteciliği tespit yöntemi. NÖHÜ Müh. Bilim. Derg. 2024;13(1):350-8.

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