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Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma

Year 2021, Volume: 9 Issue: 5, 1695 - 1706, 31.10.2021
https://doi.org/10.29130/dubited.897437

Abstract

Son yıllarda teknolojide meydana gelen gelişmelerle beraber başta internet ve sosyal medya olmak üzere bulut bilişim, akıllı telefon ve navigasyon sistemleri gibi uygulamaların kullanım oranları artmıştır. İnternet ve bilişim cihazlarının yoğun kullanımı, beraberinde depolanan veya aktarılan veri miktarını arttırmış ve bu artış aynı zamanda dijital dünya ile ilişkilendirilen suç oranının da yükselmesine neden olmuştur. İşlenen suçlara ilişkin elde edilen delil boyutu da paralel olarak artmış ve artan veri miktarı, adli bilişim uzmanlarının mevcut imkânlarla veriyi analiz edebilmesini zorlaştırmıştır. Adli bilişim veri inceleme süreçlerinde yaşanan aksamalar nihai olarak adli yargılama süreçlerini de olumsuz etkilemiştir. Söz konusu sorunların giderilmesi kapsamında makalede, elde edilen görüntü verilerinin hızlı ve doğru olarak analiz edilmesini sağlayan bir model önerilmiştir. Önerilen model, VGG16 ağ yapısı ile görüntü sınıflandırma için özel tasarlanan ağ katmanlarından oluşmaktadır. Çalışmada, 2085’i Kaggle platformundan 915’i farklı kaynaklardan oluşturulan 300*300 piksel çözünürlüklü resimlerden oluşan veri seti kullanılmıştır. Model, FloydHub ortamında Keras ve TensorFlow kütüphaneleri ile test edilmiştir. Test sonuçlarına göre modelde %97.8 doğruluk oranı elde edilmiştir. Elde edilen sonuç, benzer çalışmalarla kıyaslanmış ve önerilen modelin diğer çalışmalara oranla ortalama %5 oranında performans artışı sağladığı görülmüştür.

References

  • [1] V. Ganesh, “Artificial intelligence applied to computer,” International Journal of Advance Research in Computer Science and Management Studies, vol. 5, no. 5, pp. 21-29, 2017.
  • [2] R.M.A. Mohammad, M. Alqahtani, “A comparison of machine learning techniques for file system forensics analysis,” Journal of Information Security and Applications, vol. 46, pp. 53-61, 2019.
  • [3] W.D. Ferreire, C.B.R. Ferreire, G.C. Junior and F. Soares, “A review of digital image forensics,” Computers and Electrical Engineering, vol. 85, no. 106685, 2020.
  • [4] Y. Başar, “Siber suç soruşturmalarında adli bilişim incelemeleri,” Yüksek Lisans tezi, Fen Bilimleri Enstitüsü, Afyon Kocatepe Üniversitesi, Afyon, Türkiye, 2015.
  • [5] N. Kansagara and S. Singh, “Thematically clustering in digital forensics text string searching: A survey,”International Journal of Advanced Research in Computer Science, vol. 8, no. 3, pp. 1128-1130, 2017.
  • [6] Y. Korkmaz ve B. Aytuğ, “Adli bilişim açısından ses incelemeleri,” Science and Engineering Journel of Fırat University, c. 30, s. 1, ss. 329-343, 2018.
  • [7] A.L.S. Orozco, C.Q. Huaman, D.P. Alvarez and L.J.G Villalba, “A machine learning forensics technique to detect post-processing in digital videos,” Future Generation Computer Systems, vol. 111, pp. 199–212, 2020.
  • [8] D. Chaves, E. Fidalgo, E. Alegre, R.A. Rodriguez, F.J. Martino and G. Azzopardi, “Assessment and estimation of face detection performance based on deep learning for forensic applications,” Sensors, vol. 20, no. 16, pp. 4491, 2019.
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  • [10] K. Fernandes, J.S. Cardoso and B.S. Astrup, “A deep learning approach for the forensic evaluation of sexual assault,” Pattern Analysis and Applications, vol. 21, pp. 629–640, 2018.
  • [11] J.Y. Sun, S.W. Kim, S.W. Lee and S.J. Ko, “A novel contrast enhancement forensics based on convolutional neural networks,” Signal Processing: Image Communication, vol. 63, pp. 149–160, 2018.
  • [12] M. Bedeli, Z. Geradts and E. Eijk, “Clothing identification via deep learning: Forensic applications,” Forensic Sciences Research, vol. 3, no. 3, pp. 219–229, 2018.
  • [13] R. Olmos, S. Tabik and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing , vol. 275, pp. 66–72, 2018.
  • [14] A. Fydanaki and Z. Geradts, “Evaluating OpenFace: An open-source automatic facial comparison algorithm for forensics,” Forensic Sciences Research, vol. 3, no. 3, pp. 202–209, 2018.
  • [15] O. Mayer and M.C. Stamm, “Forensic similarity for digital images,” IEEE Transactions On Information Forensics And Security, vol. 15, pp. 1331-1346, 2020.
  • [16] P. Glomb, M. Romaszewski, M. Cholewa and K. Domino, “Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns,” Forensic Science International, vol. 290, pp. 227–237, 2018.
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  • [19] S. Karakuş, “Derin öğrenme yöntemleri kullanarak dijital deliller üzerinde adli bilişim incelemesi,” Yüksek Lisans tezi, Fen Bilimleri Enstitüsü, Fırat Üniversitesi, Elazığ, Türkiye, 2018.
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  • [21] M.K.Raji, “Digital forensic tools & cloud-based machine learning for analyzing crime data,” M.S. thesis, Department of Information Technology, Georgia Southern University, Georgia, USA, 2018.
  • [22] P.H. Rughani, “Artificial intelligence based digital forensics framework,” International Journal of Advanced Research in Computer Science, vol. 8, no. 8, pp. 10-14, 2017.
  • [23] S. Costantini, G. Gasperis and R. Olivieri, “Digital forensics and investigations meet artificial ntelligence,” Annals of Mathematics and Artificial Intelligence, vol. 86, pp. 193–229, 2019.
  • [24] A. Pandey,S. Mujmer, P. Gyarsiya and S. Kanungo, “A study on digital forensics using various algorithms for malware detection,” International Journal of Advanced Research in Computer Science, vol. 9, no. 3, pp. 85-89, 2018.
  • [25] N.M. Karie, V.R. Kebande and H.S. Venter, “Diverging deep learning cognitive computing techniques into cyber forensics,” Forensic Science International: Synergy, vol. 1, pp. 61-67, 2019.
  • [26] S.Sasank. (2019, February 01). Guns dataset [Online]. Available: https://www.kaggle.com/issaisasank/guns-object-detection.
  • [27] A.Kumar. (2020, February 02). Guns detection dataset (2nd ver.) [Online]. Available: https://www.kaggle.com/atulyakumar98/gundetection.
  • [28] Y.Khatri. (2020, October 02). Guns dataset (1th ver.) [Online]. Available: https://www.kaggle.com/khatriyash/csgo-guns-dataset.
  • [29] S.Shekhar. (2020, March 02). Knife dataset (1th ver.) [Online]. Available: https://www.kaggle.com/shank885/knife-dataset.
  • [30] V.Singh. (2018, October 08). Knife detection (1th ver.) [Online]. Available: https://www.kaggle.com/vijaysingh888/knife-detection.

Artificial Intelligence in Digital Forensics Investigation Processes: Image Classification with VGG16

Year 2021, Volume: 9 Issue: 5, 1695 - 1706, 31.10.2021
https://doi.org/10.29130/dubited.897437

Abstract

With the recent developments in technology, the usage rates of cloud computing, smartphones, and navigation systems, especially the internet and social media, have increased. The intensive use of these devices has increased the amount of data stored or transferred. Such an increase has also led to a growth in the digital world-related crime rate. The size of the evidence has grown incrementally, and making it difficult to analyze the data effectively. The failures in analysis processes have ultimately affected the judicial proceedings negatively. To solve the aforementioned problems, a model is proposed that enables fast and accurate analysis of image data in the article. The model consists of VGG16 convolutional and fully connected neural network layers. In the study, a data set consisting of 300x300 pixel resolution images, 2085 of which were compiled from the Kaggle platform and 915 from different sources, was used. The model was tested in the FloydHub with the Keras and TensorFlow libraries. According to the test results, a 97.8% accuracy rate was obtained. Test results indicate that the proposed model provides an average performance increase of 5% compared to other studies.

References

  • [1] V. Ganesh, “Artificial intelligence applied to computer,” International Journal of Advance Research in Computer Science and Management Studies, vol. 5, no. 5, pp. 21-29, 2017.
  • [2] R.M.A. Mohammad, M. Alqahtani, “A comparison of machine learning techniques for file system forensics analysis,” Journal of Information Security and Applications, vol. 46, pp. 53-61, 2019.
  • [3] W.D. Ferreire, C.B.R. Ferreire, G.C. Junior and F. Soares, “A review of digital image forensics,” Computers and Electrical Engineering, vol. 85, no. 106685, 2020.
  • [4] Y. Başar, “Siber suç soruşturmalarında adli bilişim incelemeleri,” Yüksek Lisans tezi, Fen Bilimleri Enstitüsü, Afyon Kocatepe Üniversitesi, Afyon, Türkiye, 2015.
  • [5] N. Kansagara and S. Singh, “Thematically clustering in digital forensics text string searching: A survey,”International Journal of Advanced Research in Computer Science, vol. 8, no. 3, pp. 1128-1130, 2017.
  • [6] Y. Korkmaz ve B. Aytuğ, “Adli bilişim açısından ses incelemeleri,” Science and Engineering Journel of Fırat University, c. 30, s. 1, ss. 329-343, 2018.
  • [7] A.L.S. Orozco, C.Q. Huaman, D.P. Alvarez and L.J.G Villalba, “A machine learning forensics technique to detect post-processing in digital videos,” Future Generation Computer Systems, vol. 111, pp. 199–212, 2020.
  • [8] D. Chaves, E. Fidalgo, E. Alegre, R.A. Rodriguez, F.J. Martino and G. Azzopardi, “Assessment and estimation of face detection performance based on deep learning for forensic applications,” Sensors, vol. 20, no. 16, pp. 4491, 2019.
  • [9] G. Sreenu and M.A.S. Durai, “Intelligent video surveillance: A review through deep learning techniques for crowd analysis,” J Big Data, vol. 6, no. 48, 2019.
  • [10] K. Fernandes, J.S. Cardoso and B.S. Astrup, “A deep learning approach for the forensic evaluation of sexual assault,” Pattern Analysis and Applications, vol. 21, pp. 629–640, 2018.
  • [11] J.Y. Sun, S.W. Kim, S.W. Lee and S.J. Ko, “A novel contrast enhancement forensics based on convolutional neural networks,” Signal Processing: Image Communication, vol. 63, pp. 149–160, 2018.
  • [12] M. Bedeli, Z. Geradts and E. Eijk, “Clothing identification via deep learning: Forensic applications,” Forensic Sciences Research, vol. 3, no. 3, pp. 219–229, 2018.
  • [13] R. Olmos, S. Tabik and F. Herrera, “Automatic handgun detection alarm in videos using deep learning,” Neurocomputing , vol. 275, pp. 66–72, 2018.
  • [14] A. Fydanaki and Z. Geradts, “Evaluating OpenFace: An open-source automatic facial comparison algorithm for forensics,” Forensic Sciences Research, vol. 3, no. 3, pp. 202–209, 2018.
  • [15] O. Mayer and M.C. Stamm, “Forensic similarity for digital images,” IEEE Transactions On Information Forensics And Security, vol. 15, pp. 1331-1346, 2020.
  • [16] P. Glomb, M. Romaszewski, M. Cholewa and K. Domino, “Application of hyperspectral imaging and machine learning methods for the detection of gunshot residue patterns,” Forensic Science International, vol. 290, pp. 227–237, 2018.
  • [17] M. Eriş, “Derin öğrenme yöntemleri kullanarak adli bilişim incelemelerinde delil çıkarımının gerçekleştirilmesi,” Yüksek Lisans tezi, Fen Bilimleri Enstitüsü, Fırat Üniversitesi, Elazığ, Türkiye, 2018.
  • [18] R. Thakur and R. Rohilla, “Recent advances in digital image manipulation detection techniques: A brief review,” Forensic Science International, vol. 312, no. 110311, 2020.
  • [19] S. Karakuş, “Derin öğrenme yöntemleri kullanarak dijital deliller üzerinde adli bilişim incelemesi,” Yüksek Lisans tezi, Fen Bilimleri Enstitüsü, Fırat Üniversitesi, Elazığ, Türkiye, 2018.
  • [20] M. Babiker, E. Kararslan and Y. Hoşcan, “A hybrid feature-selection approach for finding the digital evidence of web application attacks,” Turkish Journal of Electrical Engineering & Computer Sciences, vol. 27, pp. 4102 – 4117, 2019.
  • [21] M.K.Raji, “Digital forensic tools & cloud-based machine learning for analyzing crime data,” M.S. thesis, Department of Information Technology, Georgia Southern University, Georgia, USA, 2018.
  • [22] P.H. Rughani, “Artificial intelligence based digital forensics framework,” International Journal of Advanced Research in Computer Science, vol. 8, no. 8, pp. 10-14, 2017.
  • [23] S. Costantini, G. Gasperis and R. Olivieri, “Digital forensics and investigations meet artificial ntelligence,” Annals of Mathematics and Artificial Intelligence, vol. 86, pp. 193–229, 2019.
  • [24] A. Pandey,S. Mujmer, P. Gyarsiya and S. Kanungo, “A study on digital forensics using various algorithms for malware detection,” International Journal of Advanced Research in Computer Science, vol. 9, no. 3, pp. 85-89, 2018.
  • [25] N.M. Karie, V.R. Kebande and H.S. Venter, “Diverging deep learning cognitive computing techniques into cyber forensics,” Forensic Science International: Synergy, vol. 1, pp. 61-67, 2019.
  • [26] S.Sasank. (2019, February 01). Guns dataset [Online]. Available: https://www.kaggle.com/issaisasank/guns-object-detection.
  • [27] A.Kumar. (2020, February 02). Guns detection dataset (2nd ver.) [Online]. Available: https://www.kaggle.com/atulyakumar98/gundetection.
  • [28] Y.Khatri. (2020, October 02). Guns dataset (1th ver.) [Online]. Available: https://www.kaggle.com/khatriyash/csgo-guns-dataset.
  • [29] S.Shekhar. (2020, March 02). Knife dataset (1th ver.) [Online]. Available: https://www.kaggle.com/shank885/knife-dataset.
  • [30] V.Singh. (2018, October 08). Knife detection (1th ver.) [Online]. Available: https://www.kaggle.com/vijaysingh888/knife-detection.
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

İsrafil Dilber 0000-0003-4455-8834

Aydın Çetin 0000-0002-8669-823X

Publication Date October 31, 2021
Published in Issue Year 2021 Volume: 9 Issue: 5

Cite

APA Dilber, İ., & Çetin, A. (2021). Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, 9(5), 1695-1706. https://doi.org/10.29130/dubited.897437
AMA Dilber İ, Çetin A. Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma. DUBİTED. October 2021;9(5):1695-1706. doi:10.29130/dubited.897437
Chicago Dilber, İsrafil, and Aydın Çetin. “Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 Ile Görüntü Sınıflandırma”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi 9, no. 5 (October 2021): 1695-1706. https://doi.org/10.29130/dubited.897437.
EndNote Dilber İ, Çetin A (October 1, 2021) Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9 5 1695–1706.
IEEE İ. Dilber and A. Çetin, “Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma”, DUBİTED, vol. 9, no. 5, pp. 1695–1706, 2021, doi: 10.29130/dubited.897437.
ISNAD Dilber, İsrafil - Çetin, Aydın. “Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 Ile Görüntü Sınıflandırma”. Düzce Üniversitesi Bilim ve Teknoloji Dergisi 9/5 (October 2021), 1695-1706. https://doi.org/10.29130/dubited.897437.
JAMA Dilber İ, Çetin A. Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma. DUBİTED. 2021;9:1695–1706.
MLA Dilber, İsrafil and Aydın Çetin. “Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 Ile Görüntü Sınıflandırma”. Düzce Üniversitesi Bilim Ve Teknoloji Dergisi, vol. 9, no. 5, 2021, pp. 1695-06, doi:10.29130/dubited.897437.
Vancouver Dilber İ, Çetin A. Adli Bilişim İncelenme Süreçlerinde Yapay Zeka Kullanımı: VGG16 ile Görüntü Sınıflandırma. DUBİTED. 2021;9(5):1695-706.