ارزیابی کارایی تشخیص جعل کپی- انتقال تصاویر مبتنی بر بلاک بندی

شاه بهرامی, اسدالله; هویدا, فرزانه

doi:10.22052/7.1.62

ارزیابی کارایی تشخیص جعل کپی- انتقال تصاویر مبتنی بر بلاک بندی

نویسندگان

10.22052/7.1.62

چکیده

جعل کپی – انتقال، نوع خاصی از تحریف است که در آن قسمتی از تصویر کپی شده، در قسمت دیگری از همان تصویر قرار داده می شود. این نوع دستکاری به منظور پنهان کردن قطعه ناخواسته یا تکثیر یک یا چند شیء در همان تصویر انجام می گیرد. روشهای متعددی برای تشخیص جعل کپی- انتقال ارائه شده است؛ که شامل دو روش مبتنی بر بلوک و مبتنی بر نقاط کلیدی است. روش دیگر، ترکیب توصیفگرهای مختلف برای تشخیص نقاط کلیدی است. در این مقاله، یک روش تشخیص جعل کپی- انتقال براساس ویژگیهای ترکیبی ارائه شده است. از ترکیب توصیفگرهای SIFT، KAZE، HOGو Zernike در جهت آشکارسازی جعلهای کپی – انتقال استفاده می‌شود. نتایج پیاده سازی ترکیب توصیفگرها روی تصاویر جعل شده، نشان میدهد که این نوع ترکیب باعث افزایش دقت تشخیص نسبت به استفاده تک تک و دو به دوی توصیفگرها می گردد؛ اما این کارایی و دقت بالا با هزینه زیاد محاسبات به دست می‌آید. لذا، در جهت کاهش حجم محاسبات از الگوریتمهای ژنتیک در جهت بهینه سازی توصیفگرها و از الگوریتم تحلیل مولفه اصلی برای کاهش ابعاد ویژگیها استفاده شد، نتایج نشان میدهد که HOG با دقت 93.59 نسبت به دیگر توصیفگرها عملکرد بهتری برای تشخیص نقاط جعل دارد. ترکیب چهار توصیفگر موجب می شود تا عملکرد سیستم به میزان سه درصد نسبت به HOG بهبود یافته و به دقت 96.29 برسد. الگوریتم ژنتیک با انتخاب بهترین ویژگیها به درصد تشخیص 96.94 رسید. با استفاده از تحلیل مولفه اصلی ابعاد ویژگیها به 35 بعد کاهش داده شد که درصد تشخیص 94.63 به دست آمد.

کلیدواژه‌ها

20.1001.1.23223707.1397.7.1.5.4

عنوان مقاله [English]

Performance evaluation of block-based copy- move image forgery detection algorithms

نویسندگان [English]

Asadollah Shahbahrami
Farzaneh Hoveyda

چکیده [English]

Copy-move forgery is a particular type of distortion where a part or portions of one image is/are copied to other parts of the same image. This type of manipulation is done to hide a particular part of the image or to copy one or more objects into the same image. There are several methods for detecting copy-move forgery, including block-based and key point-based methods. In this paper, a method for detecting copy-move forgery is presented based on hybrid features such as SIFT, KAZE, HOG, and Zernike descriptors. The experimental results of the combinational of these descriptors on the forged images show that this type of combination increases the accuracy of detection than using single and double descriptors; but this high accuracy has the cost of computational time. To reduce the computational time, a Genetic Algorithm (GA) was used to optimize the descriptors and analysis algorithm of the principal component was applied to reduce the dimensions of the features. The results show that the HOG descriptor with a precision of 93.59 has better performance than the other descriptors to detect parts. Combined the four descriptors, the performance of the system is improved by about three percentage in compared with HOG, and reaches a precision of 96.29. The GA as the selector of the best features (six features) achieved the detection rate of 96.94. Using the principal component analysis, the dimensions of the features were reduced to 35, with a detection rate of 94.63.

کلیدواژه‌ها [English]

Copy-Move
Block-based
Evaluation
Detection algorithms
Forgery

مراجع

[1] Nor Bakiah, A., Ainuddin, W., Mohd, Y., al.,”Copy-Move Forgery Detection: Survey, Challenges and Future Directions”, School of Computer Science, Journal of Network and Computer Applications, 13 September 2016. [2] Yanga, F., Jingwei, L., Wei, L., Wengb, J., “Copy-move forgery detection based on hybrid features”, Elsevier Ltd, pp.73-83, 2017. [3] Oreski, S. and Oreski.,G., “Genetic algorithm-based heuristic for feature selection in credit risk assessment”, Expert Systems with Applications, vol. 41, pp. 2052-2064, 2014. [4] Espejo, P.G., Ventura, S., Herrera, F., “A survey on the application of genetic programming to classification”, IEEE Transactions on Systems, Many, and Cybernetics Part C: Applications and Reviews 40, 121–144, 2010. [5] Poon, B., Ashraful, Amin., Yan, H., “Improved Methods on PCA Based Human Face Recognition for Distorted Images”, Proceedings of the International MultiConference of Engineers and Computer Scientists, Vol. 1, 2016. [6] S.C.Ng, “Principal Component analysis to reduce dimension on digital image”, Procedia Computer Science, pp.113-119, 2017. [7] Mohan, M., Preetha, VH.,” Gabor Filter – HOG Based Copy Move Forgery Detection”, IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), pp. 41-45, 2017. [8] Amerini, I., Ballan,L., Caldelli,R., Bimbo, A.D., Serra, G., “A SIFT-based forensic method for copy-move attack detection and transformation recovery”, IEEE Trans.Inf. Forensics Secur. 6 (3), 1099–1110, 2011. [9] Alcantarilla, P.F., Bartoli,A., Davison, A.J., “KAZE features”, In: Proceedings of the European Conference on Computer Vision. pp. 214–227, 2012. [10] Ryu ,S., Lee, M., Lee,H., “Detection of Copy- Rotate-Move Forgery Using Zernike Moments”, Springer, International Workshop on Information Hiding, pp. 51-65, 2010. [11] Zhao,Y., Wang,S., Zhang, X., Yao, H., “Robust hashing for image authentication using Zernike moments and local features”, IEEE Trans Inform Forensics Secur, 2013;8:55–63. [12] Muhammad, G., Hussain, M., “Passive detection of copy–move image forgery using undecimated wavelets and zernike moments”, In Inform Instit (Tokyo) Inform, 16(5):1343–4500, 2013. [13] Contter, V., et, “Active, and passive multimedia forensics”, University of Trento, 2011. [14] sawadi, Al.,MS., “Automatic detection of copy-move Image forgery based on clustering technique”,king saud university,2013. [15] Popescu, A. and Farid, H., “Exposing digital forgeries by detecting duplicated image regions”, Dept.Comput.Sci., Dartmouth College, Tech, Rep, 2004. [16] Zimba, M. and X in grning, S., “DWT-PCA(EVD)based copy-move image forgery detection”, International Journal of Digital Content Technology and its Applications, Vol 5,No 1,pp 251-25,2011. [17] Kang, B. and Wei, S., “Identifying tampered regions using singular value decomposition in digital image forensics”, in Computer Science and Software Engineering, International Conference on, Vol 3, PP 926 – 930, 2008. [18] Davarzani, R., Yaghmaie, K., Mozaffari, S., Tapak, M., “Copy-move forgery detection using multiresolution local binary patterns”, Forensic Sci Int.,231:61–72, 2013. [19] Christlein, V., Riess, C. and Angelopoulou, “on rotation invariance in copy-move forgery detection”, in information forensics and security (WIFS), IEEE international workshop on, pp 1-6, 2010. [20] Espejo, P.G., Ventura, S., Herrera, F., “A survey on the application of genetic programming to classification”, IEEE Transactions on Systems, Many, and Cybernetics Part C: Applications and Reviews 40 (2) 121–144,2010. [21] Shabanifard, M., Shayesteh, M., Akhaee, A., “Forensic detection of image manipulation using the Zernike moments and pixel-pair histogram”.IET Image Process. 2013,7:817–828. [22] Cao, Y., et al., “A robust detection algorithm for copy-move forgery in digital images”, Forensic science international, Vol 214, No 1, pp 33-43, 2012. [23] Sreelakshmy, I. J. and Kovoor, B.C., “Hybrid Method for Copy-Move Forgery Detection in Digital Images”, Springer Nature Switzerland AG 2019. [24] Wang, ch., Zhang, zhi. and Zhou, x., “An Image Copy-Move Forgery Detection Scheme Based on A-KAZE and SURF Features”, School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China, 3 December 2018. [25] Chun Lee, J., Chang, Ch., Chen, W., “Detection of copy–move image forgery using histogram of orientated gradients”, Elsevier Ltd, 8 March, 2015. [26] Zhang, Z., Wang, G., Yu, Z., “A novel model for splicing detection”, In IEEE 5th international splicing detection, conference on bio-inspired computing: theories and applications (BIC-TA), 2010, Changsha, China, 962–965. [27] Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G., “sift-based forensic method for copy–move attack detection and transformation recovery”, IEEE Trans Inform Forensics Secur.2011;6(3):1099–1110. [28] Pan, X., Lyu, S., “Detecting image region duplication using SIFT features”, In IEEE international conference on acoustics speech and signal processing (ICASSP); 2010; Texas, USA; 1706–1709. [29] Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Serra, G., “Copy-move forgery detection and localization by means of robust clustering with J-linkage”, Signal Process:Image Commun. 2013;28:659–669. [30] Costanzo, A., Amerini, I., Caldelli, R., Barni, M., “Forensic analysis of SIFT keypoint removal and injection”, IEEE Trans. Inform Forensics Secur. 2014; 9:1450–1464. [31] Lynch, G., Shih, FY., Liao, H-Y M., “ An efficient expanding block algorithm for image copy-move forgery detection”, Inf Sci. 2013;239:253–265. [32] Pan, X. and Lyu, S., “ Region duplication detection using image feature matching”, IEEE Transactions on Information Forensics and Security, Vol 5, No 4, PP 857-867,2010. [33] Ketenci, S., Ulutas, G., “Copy-move forgery detection in images via 2D-Fourier transform”, In 36th International Conference on Telecommunications and Signal Processing (TSP); 2013; Rome, Italy; 813–816. [34] Kang, X., Stamm, MC., Peng, A., Liu, KR., “Robust median filtering forensics using an autoregressive model”, IEEE Trans Inform Forensics Secur. 2013;8:1456–1468. [35] Thuong Le-Tien, Hanh Phan-Xuan, Thuy Nguyen-Chinh, and Thien Do-Tieu,“Image Forgery Detection: A Low Computational-Cost and Effective Data-Driven Model”, International Journal of Machine Learning and Computing, Vol. 9, No. 2, April 2019.