SAYISAL İMGELER İÇİN PRNU VE CNN TABANLI BÖLGESEL MÜDAHALE TESPİTİ

Abstract

Sayısal resimler üzerinde yapılan çeşitli oynamaları tespit edebilmek, gelişen yazılımların karmaşıklığından ötürü oldukça zorlaşmaktadır. Bu karmaşıklığa çözüm olarak klasik müdahale tespiti yöntemlerine ek olarak son yıllarda evrişimsel sinir ağı tabanlı yöntemler geliştirilmiştir. Böylelikle çok karmaşık müdahaleleri bile tespit edebilen ağlar eğitilebilmiştir. Bu makalede, küçük boyutlarda pencere kullanarak bölgesel müdahale tespiti yapabilen klasik yöntemlerden olan, kameranın kendisine ait olan sensörlerinden elde edilen parmakizini kullanan sensör tabanlı PRNU(Photo Response Non Uniformity) yöntemi ile yeni bir yaklaşım olan evrişimsel sinir ağı(CNN) tabanlı kamera model sınıflandırıcısı yöntemi karşılaştırılmıştır. Böylelikle hangi yöntemin daha başarılı olduğu detaylıca ortaya koyulmuştur. Toplamda 26 adet kamera modeli ve bu kamera modellerinden seçilen 96 x 96’lık piksel blokları ile eğitilen CNN modeli, hem 96 hem de 128’lik pencere boyutu kullanılarak çalışan PRNU yöntemi ile kıyaslanmıştır. Bu kıyaslama sonucunda bölgesel müdahale tespiti probleminde CNN tabanlı kamera model sınıflandırıcısının PRNU yöntemine göre daha başarılı olduğu gösterilmiştir. 

Keywords

• Lokal müdahale tespiti,evrişimsel sinir ağı,PRNU,oynanmış bölge tespiti,kamera model sınıflandırıcısı,derin öğrenme

PRNU and CNN Based Local Tamper Detection For Digital Images

Abstract

Detecting various forgeries on digital images is becoming more difficult due to the complexity of developing software. As a solution to this complexity, in addition to conventional detection methods, convolutional neural network (CNN) based methods have been developed in recent years. Thus, networks capable of detecting even very complex interventions could be trained. In this paper, a new approach to the convolutional neural network (CNN) based camera model classifier method is compared with the sensor-based PRNU (Photo Response Non Uniformity) method, which is one of the classical methods that can detect local detection using small-scale windows. Thus, which method is more successful is revealed in detail. A total of 26 camera models and the CNN model, which was trained with 96 x 96 pixel blocks selected from these camera models, was compared with the PRNU method using both the 96 and 128 window size. As a result of this comparison, CNN based camera model classifier has been shown to be more successful than PRNU method in the local tamper detection problem.

Keywords

• Local tamper detection,convolutional neural network,PRNU,image forgery,camera model classifier,deep learning

References

1. 1. Bayar, B., & Stamm, M. C. (2016, June). A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security (pp. 5-10). ACM. DOI:10.1145/2909827.2930786
2. 2. Bondi, L., Güera, D., Baroffio, L., Bestagini, P., Delp, E. J., & Tubaro, S. (2017). A preliminary study on convolutional neural networks for camera model identification. Electronic Imaging, 2017(7), 67-76. DOI: https://doi.org/10.2352/ISSN.2470-1173.2017.7.MWSF-327
3. 3. Bondi, L., Lameri, S., Güera, D., Bestagini, P., Delp, E. J., & Tubaro, S. (2017, July). Tampering detection and localization through clustering of camera-based CNN features. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (pp. 1855-1864). IEEE. DOI: 10.1109/CVPRW.2017.232
4. 4. Bondi, L., Baroffio, L., Güera, D., Bestagini, P., Delp, E. J., & Tubaro, S. (2017). First steps toward camera model identification with convolutional neural networks. IEEE Signal Processing Letters, 24(3), 259-263. DOI: 10.1109/LSP.2016.2641006
5. 5. Dirik, A. E., & Memon, N. (2009, November). Image tamper detection based on demosaicing artifacts. In Image Processing (ICIP), 2009 16th IEEE International Conference on (pp. 1497-1500). IEEE. DOI: 10.1109/ICIP.2009.5414611
6. 6. Farid, H. (2009). Exposing digital forgeries from JPEG ghosts. IEEE transactions on information forensics and security, 4(1), 154-160. DOI: 10.1109/TIFS.2008.2012215
7. 7. Ferrara, P., Bianchi, T., De Rosa, A., & Piva, A. (2012). Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Transactions on Information Forensics and Security, 7(5), 1566-1577. DOI: 10.1109/TIFS.2012.2202227
8. 8. Gloe, T., & Böhme, R. (2010, March). The'Dresden Image Database'for benchmarking digital image forensics. In Proceedings of the 2010 ACM Symposium on Applied Computing (pp. 1584-1590). ACM. Doi:10.1145/1774088.1774427

9. 9. Goljan, M., Fridrich, J., & Filler, T. (2009, February). Large scale test of sensor fingerprint camera identification. In Media Forensics and Security (Vol. 7254, p. 72540I). International Society for Optics and Photonics. Doi: 10.1117/12.805701
10. 10. Krawetz, N., & Solutions, H. F. (2007). A Picture’s Worth... Hacker Factor Solutions, 6.
11. 11. Lin, Z., He, J., Tang, X., & Tang, C. K. (2009). Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recognition, 42(11), 2492-2501. Doi:https://doi.org/10.1016/j.patcog.2009.03.019
12. 12. Lukas, J., Fridrich, J., & Goljan, M. (2006). Digital camera identification from sensor pattern noise. IEEE Transactions on Information Forensics and Security, 1(2), 205-214. DOI: 10.1109/TIFS.2006.873602
13. 13. Tuama, A., Comby, F., & Chaumont, M. (2016, December). Camera model identification with the use of deep convolutional neural networks. In Information Forensics and Security (WIFS), 2016 IEEE International Workshop on (pp. 1-6). IEEE. DOI: 10.1109/WIFS.2016.7823908
14. 14. Vedaldi, A., & Lenc, K. (2015, October). Matconvnet: Convolutional neural networks for matlab. In Proceedings of the 23rd ACM international conference on Multimedia (pp. 689-692). ACM. Doi:10.1145/2733373.2807412
15. 15. Liu, Y., Guan, Q., Zhao, X., & Cao, Y. (2018, June). Image forgery localization based on multi-scale convolutional neural networks. In Proceedings of the 6th ACM Workshop on Information Hiding and Multimedia Security (pp. 85-90). ACM. Doi:10.1145/3206004.3206010
16. 16. Ye, S., Sun, Q., & Chang, E. C. (2007, July). Detecting digital image forgeries by measuring inconsistencies of blocking artifact. In Multimedia and Expo, 2007 IEEE International Conference on (pp. 12-15). IEEE. DOI: 10.1109/ICME.2007.4284574