Görüntü İncelemesine Göre Sorgulama: Dijital Görüntü Tabanlı Adli Görüntülerin Derin Öğrenme Yöntemleri Kullanılarak Sınıflandırılması

Abstract

Bilgi sistemlerinin ve çevrimiçi hizmetlerin kullanımındaki sonsuz artış, bilgi aktarımı için temel platformlardan biri olan dijital ve görüntü içeren verilerin adli incelemelerini de tetiklemiştir. Adli görüntü inceleme temel olarak bilimsel yöntemlerin ve adli inceleme yazılımlar kullanılarak ilgili görüntüler hakkında delil oluşturulmasını sağlayan bilimsel bir disiplindir. Özellikle, son raporlara göre, adli vaka sonucunda delil niteliği taşıyan ve olayın aydınlanmasını sağlayabilecek her türlü kayıt cihazından elde edilmiş görüntülerin incelenmesi ve suç unsuru olanlarının tespiti artan veri miktarı nedeniyle giderek büyük bir problem haline gelmektedir. Bu çalışmada katkımız iki katkı sunmaktadır. İlk olarak dijital görüntülerin altı farklı model kullanarak sekiz farklı suç kategorisi olarak sınıflandıran yeni bir yaklaşım sunuyor. İkincisi, suçların sınıflandırılması için yeni bir veri kümesinin oluşturarak paylaşıma sunuyor. Çalışma boyunca, Bet, ChildAbuse, kredi kartı ve bankacılık, uyuşturucu, korkutucu, bıçak, pornografik ve silah dâhil olmak üzere 8 farklı suç Kategorisine ait toplam 15.065 görüntü örneğini kapsayan yeni veri setimizi kullanıldı. Suç görüntülerini sınıflandırmak için bu çalışmada 6 farklı model kullanılmıştır. CNN modeli kendimiz ve öğrenmeyi ince ayarlara aktarmak için kullanılan diğer beş model tarafından yaratılmıştır. Görüntü sınıflandırma görevleri için VGG16, VGG19, Xception modeli, InceptionResNetV2 ve NASNetLarge önceden eğitilmiş ağ modeli parametreleri kullanıldı. Ayrıca, bu modellerin performansı test doğruluğu ve zaman ölçümleri kullanılarak karşılaştırılır. Sonuçlar, NASNetLarge modeli kullanarak %89.74’e kadar tahmin doğruluğu elde edilmiştir.

Keywords

• Görüntü işleme
• derin öğrenme yöntemi
• görüntü sınıflandırması
• veri madenciliği
• adli inceleme

Query by Image Examination: Classification of Digital Image-Based Forensics Using Deep Learning Methods

Abstract

The continuous increase in the use of information systems and online services has also spurred the forensic examination of digital and image data, which serves as the primary platform for information transfer. In particular, according to the latest reports, the examination of the images obtained from all kinds of recording devices that have the quality of evidence as a result of the forensic case and that can provide the clarification of the incident and the detection of the criminal elements are becoming a critical problem due to the huge amount of data. Our contribution in this study is two-folded. First, we present a new approach that classifies digital images into eight different crime categories using six different models. Second, we have created a new dataset for the classification of crimes and opened it to the public. Throughout the study, we have used our new dataset which has a total of 15,065 image samples from 8 different crime categories including Bet, ChildAbuse, Credit Card and Banking, Drugs, Frightening, Knives, Pornographic and Weapons. In this study, six different models were used to classify crime images. The CNN model was developed by us and five other models used for transfer learning. Pre-trained network model parameters VGG16, VGG19, Xception Model, InceptionResNetV2 and NASNetLarge were used for crime image classification tasks. In addition, the performance of these models is compared using test accuracy and time metrics. Resultly, we achieved prediction accuracy of up to 89.74% using the NASNetLarge model.

Keywords

• image processing
• deep learning method
• image classification
• data mining
• forensic investigation

References

1. adfsolutio, nshttps://www.adfsolutions.com/, 2021. google scholar
2. Belkasoft, https://belkasoft.com/, 2021. google scholar
3. Birajdar, G.K., & Mankar, V.H. (2013). Digital image forgery detection using passive techniques: A survey. Digital investigation, 10(3), 226-245. google scholar
4. Cao, H., & Kot, A.C. 2009. Accurate detection of demosaicing regularity for digital image forensics. IEEE Transactions on Information Forensics and Security, 4(4), 899-910. google scholar
5. Chandra, M., Pandey, S., Chaudhary, R. (2010). Digital watermarking technique for protecting digital images. In 2010 3rd International Conference on Computer Science and Information Technology 7, 226-233. IEEE. google scholar
6. Choodum, A., Boonsamran, P., NicDaeid, N., Wongniramaikul, W. (2015). On-site semi-quantitative analysis for ammonium nitrate detection using digital image colourimetry. Science & Justice, 55(6), 437-445. google scholar
7. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258). google scholar
8. Coulibaly, S., Kamsu-Foguem, B., Kamissoko, D., Traore, D. (2019). Deep neural networks with transfer learning in millet crop images. Computers in Industry, 108, 115-120. google scholar

9. Europol, (2020). How Are Organised Crime Groups Involved in Sports Corruption?. https://www.europol.europa.eu/newsroom/news/how-areorganised-crime-groups-involved-in-sports-corruption. google scholar
10. de Castro Polastro, M., da Silva Eleuterio, P.M. (2010). Nudetective: A forensic tool to help combat child pornography through automatic nudity detection. In 2010 Workshops on Database and Expert Systems Applications, 349-353. IEEE. google scholar
11. Del Mar-Raave, J. R., Bahşi, H., Mrsic, L., Hausknecht, K. 2021. A machine learning-based forensic tool for image classification-A design science approach. Forensic Science International: Digital Investigation, 38, 301265. google scholar
12. Dey, T., Mandal, S., Varcho, W. (2017). Improved image classification using topological persistence. In Proceedings of the conference on Vision, Modeling and Visualization, 161-168). google scholar
13. Ferreira, W.D., Ferreira, C.B., da Cruz Junior, G., Soares, F. (2020). A review of digital image forensics. Computer & Electrical Engineering, 85, 106685. google scholar
14. Hafiz, R., Haque, M. R., Rakshit, A., Uddin, M. S. (2020). Image-based soft drink type classification and dietary assessment system using deep convolutional neural network with transfer learning. Journal of King Saud University-Computer and Information Sciences. 34(5), 1775-1784. google scholar
15. Garfinkel, S.L., Parker-Wood, A., Huynh, D., Migletz, J. (2010). An automated solution to the multiuser carved data ascription problem. IEEE Transactions on Information Forensics and Security, 5(4), 868-882. google scholar
16. Grillo, A., Lentini, A., Me, G., Ottoni, M. (2009). Fast user classifying to establish forensic analysis priorities. In 2009 Fifth International Conference on IT Security Incident Management and IT Forensics, 69-77. IEEE. google scholar
17. Gomez, L. S. M. (2012). Triage in-Lab: case backlog reduction with forensic digital profiling. In Proceedings of the Argentine Conference on Informatics and Argentine Symposium on Computing and Law, 217-225. google scholar
18. Isnard, A., Council, T. C. (2001). Can surveillance cameras be successful in preventing crime and controlling anti-social behaviours. In Character, Impact and Prevention of Crime in Regional Australia Conference. google scholar
19. Kara, I. (2017). A Review About Child Abuse Crimes Committed Through Internet In Turkey. Int J Forensic Sci Pathol, 5(3), 337-340. google scholar
20. Karakuş, S., Kaya, Ö. Ü., Ertam, Ö. Ü. F., Talu, M. F. (2018). Derin Öğrenme Yöntemlerinin Kullanılarak Dijital Deliller Üzerinde Adli Bilişim İncelemesi. google scholar
21. Keras, https://keras.io/api/applications/, 2021. google scholar
22. Kuhle, L.F., Oezdemir, U., Beier, K.M. (2021). Child Sexual Abuse and the Use of Child Sexual Abuse Images. In Pedophilia, Hebephilia and Sexual Offending against Children (pp. 15-25). Springer, Cham. google scholar
23. Lin, X., Li, J.H., Wang, S.L., Cheng, F., Huang, X.S. (2018). Recent advances in passive digital image security forensics: A brief review. Engineering, 4(1), 29-39. google scholar
24. Mahalakshmi, S.D., Vijayalakshmi, K., Priyadharsini, S. (2012). Digital image forgery detection and estimation by exploring basic image manipulations. Digital Investigation, 8(3-4), 215-225. google scholar
25. Marturana, F., Tacconi, S. (2013). A Machine Learning-based Triage methodology for automated categorization of digital media. Digital Investigation, 10(2), 193-204. google scholar
26. Marturana, F., Me, G., Berte, R., Tacconi, S. (2011). A quantitative approach to triaging in mobile forensics. In 2011IEEE 10th International Conference on Trust, Sec urity and Privacy in Computing and Communications (pp. 582-588). IEEE. google scholar
27. McClelland, D., Marturana, F. (2014). A Digital Forensics Triage methodology based on feature manipulation techniques. In 2014 IEEE International Conference on Communications Workshops (ICC) (pp. 676-681). IEEE. google scholar
28. McDown, R.J., Varol, C., Carvajal, L., Chen, L. (2016). In-Depth Analysis of Computer Memory Acquisition Software for Forensic Purposes. Journal Of Forensic Sciences, 61, S110-S116. google scholar
29. M Kirchner& R. Böhme (2007). Tamper hiding: Defeating image forensics In International Workshop on Information Hiding, Springer, Berlin, Heidelberg. (2007), pp.326-341. google scholar
30. Olmos, R., Tabik, S., Herrera, F. (2018). Automatic handgun detection alarm in videos using deep learning. Neurocomputing, 275, 66-72. google scholar
31. Paluszek, M., & Thomas, S. (2020). Practical Matlab deep learning. A Project-Based Approach, Michael Paluszek and Stephanie Thomas. google scholar
32. Pearson, H. (2006). Forensic software traces tweaks to images. Nature, 439(7076), 520-522. google scholar
33. Peng, F., Liu, J., Long, M. (2013). Identification of natural images and computer generated graphics based on hybrid features. In Emerging Digital Forensics Applications for Crime Detection, Prevention, and Security (pp. 18-34). IGI Global. google scholar
34. Peng, C., Liu, Y., Yuan, X., & Chen, Q. (2022). Research of image recognition method based on enhanced inception-ResNet-V2. Multimedia Tools and Applications, 81(24), 34345-34365. google scholar
35. Piva, A. (2013). An overview on image forensics. International Scholarly Research Notices, 2013. google scholar
36. Rahimzadeh, M., Parvin, S., Safi, E., Mohammadi, M.R. (2021). Wise-SrNet: A Novel Architecture for Enhancing Image Classification by Learning Spatial Resolution of Feature Maps. arXiv preprint arXiv:2104.12294. google scholar
37. Ren, S., He, K., Girshick, R., Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 28, 91-99. google scholar
38. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Fei-Fei, L. (2015). Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252. google scholar
39. Saber, A. H., Khan, M. A., & Mejbel, B. G. (2020). A survey on image forgery detection using different forensic approaches. Advances in Science, Technology and Engineering Systems Journal, 5(3), 361-370. google scholar
40. Sharma, M., & Vig, L. (2018). Automatic classification of low-resolution chromosomal images. In Proceedings of the European Conference on Computer Vision (ECCV) Workshops (pp. 0-0). google scholar
41. Sanap, V.K., & Mane, V. (2015). Comparative study and simulation of digital forensic tools. Int J Comput Appl, 975, 8887. google scholar
42. Seigfried, K.C., Lovely, R.W., Rogers, M.K. (2008). Self-Reported Online Child Pornography Behavior: A Psychological Analysis. International Journal of Cyber Criminology, 2(1). google scholar
43. Sharma, A., Singh, A., Choudhury, T., Sarkar, T. (2021). Image Classification using ImageNet Classifiers in Environments with Limited Data. google scholar
44. Simonyan, K., Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. google scholar
45. Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A.A. (2017). Inception-v4, inception-resnet and the impact of residual connections on learning. In Thirty-first AAAI conference on artificial intelligence. google scholar
46. Tammina, S. (2019). Transfer learning using vgg-16 with deep convolutional neural network for classifying images. International Journal of Scientific and Research Publications (USRP), 9(10), 143-150. google scholar
47. Thakur, R., Rohilla, R. (2020). Recent advances in digital image manipulation detection techniques: A brief review. Forensic Science International, 312, 110311. google scholar
48. Verma, G.K., Dhillon, A. (2017). A handheld gun detection using faster r-cnn deep learning. In Proceedings of the 7th International Conference on Computer and Communication Technology (pp. 84-88). google scholar
49. x-ways, https://www.x-ways.net/, 2021. google scholar
50. Wu, L.T., Parrott, A.C., Ringwalt, C L., Yang, C., Blazer, D.G. (2009). The variety of ecstasy/MDMA users: results from the National Epidemiologic Survey on alcohol and related conditions. The American Journal on Addictions, 18(6), 452-461. google scholar
51. Zhang, X., Zou, J., He, K., Sun, J. (2015). Accelerating very deep convolutional networks for classification and detection. IEEE transactions on pattern analysis and machine intelligence, 38(10), 1943-1955. google scholar
52. Zoph, B., Vasudevan, V., Shlens, J., Le, Q.V. (2018). Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 8697-8710). google scholar
53. Wang, W., Dong, J., Tan, T. (2009). A survey of passive image tampering detection. In International Workshop on Digital Watermarking (pp. 308-322). Springer, Berlin, Heidelberg. google scholar
54. Dateset, https://ilkerkara.karatekin.edu.tr/RequestDataset.htmlekle dataset, 2021. google scholar