Security of Digital Multimedia Data and Forgery Detection

Year 2023, Volume: 37 Issue: 3, 87 - 93, 18.12.2023

Fulya Akdeniz , Yaşar Becerikli

https://doi.org/10.61970/adlitip.1208157

Abstract

Research on verifying the integrity of digital multimedia data has increasing in recent years. Therefore, it has been observed that the number of studies on digital multimedia security has increasing day by day. This shows studies on digital multimedia security are still an active research area. People who have not received professional training in the fields of audio, image and video can easily modify audio, image and video data through tools such as mobile phones, smart devices, various web applications, etc. These modifications disrupt the accuracy, integrity and authenticity of the data. These integrity and authenticity corrupted data can be used for various purposes such as misleading the judicial authorities, disrupting public order, using as fake evidence in court, and deceiving automatic speaker verification systems, etc. For this reason, it is very important forgeries detection systems on digital multimedia data today. Studies have gathered forgery detection methods on digital multimedia data under two categories as active and passive techniques. In the literature, studies on passive techniques, which focus on active techniques for forgery detection on digital data, especially on audio signals, are relatively less than active techniques. In this research article, it is aimed to categorize the recent studies on copy-move and splicing forgery detection from passive techniques.

Keywords

Digital multimedia security, Digital multimedia forensic, Audio forgery detection, Image forgery detection, Video forgery detection

Project Number

121E725

Dijital Multimedya Verilerinin Güvenliği ve Sahtecilik Tespiti

Abstract

Dijital multimedya verilerinin bütünlüğünün doğrulanması konusundaki araştırmalar son yıllarda hız kazanmıştır. Buna bağlı olarak da dijital multimedya güvenliği üzerine yapılan çalışmaların sayısının gün geçtikçe arttığı gözlemlenmiştir. Bu da dijital multimedya güvenliği konusundaki çalışmaların hala güncel ve aktif bir araştırma alanı olduğunu göstermektedir. Ses, görüntü ve video alanlarında profesyonel bir eğitim almamış kişiler cep telefonları, akıllı cihazlar, çeşitli web uygulamaları vb. gibi araçlar üzerinden ses, görüntü ve video verileri üzerinde kolayca değişiklik yapabilmektedir. Yapılan bu değişiklikler ise verilerin doğruluğunu, bütünlüğünü ve gerçekliğini bozmaktadır. Bütünlüğü ve gerçekliği bozulmuş bu veriler adli makamları yanıltma, kamu düzenini bozma, mahkemede sahte delil olarak kullanma ve otomatik konuşmacı doğrulama sistemlerini yanıltma vb. gibi çeşitli amaçlar için kullanılabilmektedir. Bu sebepten günümüzde dijital multimedya verileri üzerinde yapılan sahteciliklerin tespit edilmesi oldukça önemli bir konudur. Yapılan çalışmalar, dijital multimedya verileri üzerindeki sahtecilik tespit yöntemlerini aktif ve pasif teknikler olmak üzere iki kategori altında toplamıştır. Literatürde özellikle ses sinyalleri başta olmak üzere dijital veriler üzerinde yapılan sahteciliklerin tespiti için aktif teknikler üzerine yoğunlaşıldığı pasif teknikler üzerine yapılan çalışmaların aktif tekniklere göre nispeten daha az olduğu tespit edilmiştir. Bu araştırma makalesinde pasif tekniklerden kopyala-yapıştır ve birleştirme sahtecilik tespitleri ile ilgili son yıllarda yapılmış olan çalışmaların kategorize edilmesi amaçlanmıştır.

Keywords

Dijital Multimedia Adli Bilişim, Digital Multimedia Güvenliği, Ses Sahteciliği Tespiti, Görüntü Sahteciliği Tespiti, Video Sahteciliği Tespiti.

Supporting Institution

TÜBİTAK

Project Number

121E725

Thanks

Bu çalışma 1002 programı kapsamında 121E725 nolu proje ile TÜBİTAK tarafından desteklenmiştir.

References

• Bloomberg J. Digitization, digitalization, and digital transformation: confuse them at your peril. Forbes. 2018.
• Desai SD, Pudakalakatti NR, Baligar VP. A survey on intelligent security techniques for high-definition multimedia data. Intelligent Techniques in Signal Processing for Multimedia Security. 2017;15–45. https://doi.org/10.1007/978-3-319-44790-2_2
• Zanardelli M, Guerrini F, Leonardi R, Adami N. Image forgery detection: a survey of recent deep-learning approaches. Multimedia Tools and Applications. 2022;1–46. https://doi.org/10.1007/s11042-022-13797-w
• Gupta S, Cho S, Kuo CC. J. Current developments and future trends in audio authentication. Ieee Multimedia. 2011;19(1):50–9. https://doi.org/10.1109/MMUL.2011.74
• Imran M, Ali Z, Bakhsh ST, Akram S. Blind detection of copy-move forgery in digital audio forensics. IEEE Access. 2017;5:12843–55. https://doi.org/10.1109/ACCESS.2017.2717842
• Kang X, Wei S. Identifying tampered regions using singular value decomposition in digital image forensics. In: 2008 International conference on computer science and software engineering, Vol. 3. IEEE; 2008. pp. 926–30. https://doi.org/10.1109/CSSE.2008.876
• Khan MK, Zakariah M, Malik H, Choo KK. R. A novel audio forensic data-set for digital multimedia forensics. Australian Journal of Forensic Sciences. 2018;50(5):525–42. https://doi.org/10.1080/00450618.2017.1296186
• Bourouis S, Alroobaea R, Alharbi AM, Andejany M, Rubaiee S. Recent advances in digital multimedia tampering detection for forensics analysis. Symmetry. 2020;12(11):1811. https://doi.org/10.3390/sym12111811
• Akdeniz F, Becerikli Y. Detection of copy-move forgery in audio signal with mel frequency and delta-mel frequency kepstrum coefficients. In: 2021 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE; 2021. pp. 1–6. https://doi.org/10.1109/ASYU52992.2021.9598977
• Raghavan S. Digital forensic research: current state of the art. CSI Transactions on ICT. 2013;1(1):91–114. https://doi. org/10.1007/s40012-012-0008-7
• Yerushalmy I, Hel-Or H. Digital image forgery detection based on lens and sensor aberration. Int J Comput Vis. 2011;92:71–91. https://doi.org/10.1007/s11263-010-0403-1
• Qazi EUH, Zia T, Almorjan A. Deep learning-based digital image forgery detection system. Applied Sciences. 2022;12(6):2851. https://doi.org/10.3390/app12062851
• Ali SS, Ganapathi II, Vu NS, Ali SD, Saxena N, Werghi N. Image forgery detection using deep learning by recompressing images. Electronics. 2022;11(3):403. https://doi.org/10.3390/electronics1103040393
• Fatima B, Ghafoor A, Ali SS, Riaz MM. FAST, BRIEF and SIFT based image copy-move forgery detection technique. Multimedia Tools and Applications. 2022;1–15. https://doi.org/10.1007/s11042-022-12915-y
• Rodriguez-Ortega Y, Ballesteros DM, Renza D. Copy-move forgery detection (CMFD) using deep learning for image and video forensics. Journal of Imaging. 2021;7(3):59. https://doi.org/10.3390/jimaging7030059
• Manjunatha S, Patil MM. Deep learning-based technique for image tamper detection. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV). IEEE; 2021. pp. 1278–85. https://doi.org/10.1109/ICICV50876.2021.9388471
• Barad ZJ, Goswami MM. Image forgery detection using deep learning: a survey. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE; 2020. pp. 571–76. https://doi.org/10.1109/ICACCS48705.2020.9074408
• Li, Q.; Wang, R.; Xu, D. A Video Splicing Forgery Detection and Localization Algorithm Based on Sensor Pattern Noise. Electronics. 2023, 12, 1362.
• Patel, J., & Sheth, R. (2022). Passive Video Forgery Detection Techniques to Detect Copy Move Tampering Through Feature Comparison and RANSAC. In Cyber Security and Digital Forensics (pp. 161-177). Springer, Singapore. https://doi.org/10.1007/978-981-16-3961-6_15
• Raskar, P. S., & Shah, S. K. (2021). Real time object-based video forgery detection using YOLO (V2). Forensic Science International, 327, 110979. https://doi.org/10.1016/j. forsciint.2021.110979
• Shelke, N. A., & Kasana, S. S. (2021). A comprehensive survey on passive techniques for digital video forgery detection. Multimedia Tools and Applications, 80(4), 6247-6310. https://doi.org/10.1007/s11042-020-09974-4
• Fadl, S., Han, Q., & Li, Q. (2021). CNN spatiotemporal features and fusion for surveillance video forgery detection. Signal Processing: Image Communication, 90, 116066. https://doi.org/10.1016/j.image.2020.116066
• Akdeniz, F., & Becerikli, Y. (2022, October). Linear Prediction Coefficients based Copy-Move Forgery Detection in Audio Signal. In 2022 6rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE. https://doi.org/10.1109/ ISMSIT56059.2022.9932794
• Moussa, D., Hirsch, G., & Riess, C. (2022). Towards Unconstrained Audio Splicing Detection and Localization with Neural Networks. arXiv preprint arXiv:2207.14682.
• Zhang, Z., Zhao, X., & Yi, X. (2022). ASLNet: An EncoderDecoder Architecture for Audio Splicing Detection and Localization. Security and Communication Networks, 2022. https://doi.org/10.1155/2022/8241298
• Huang, X., Liu, Z., Lu, W., Liu, H., & Xiang, S. (2020). Fast and effective copy-move detection of digital audio based on auto segment. In Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice (pp. 127-142). IGI Global. https://doi.org/10.4018/978-1-7998-3025-2.ch011