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Copy-paste forgery detection using deep learning with error level analysis

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Abstract

Image manipulation has become a common problem due to the tremendous growth of digital tools and applications. Several image forgeries can uneventfully eradicate the original contents from digital images. Copy-paste forgeries become the most challenging problem, where the image’s content is manipulated by copying and pasting from one region to another location within the same image. Existing forgery detection techniques face a high complexity in identifying the manipulated region when it is subjected to various geometrical alterations. Hence, this study introduces a novel deep learning (DL) based technique to detect the copy paste forgeries in digital images. This research undergoes three major operations pre-processing, image augmentation and classification. Image normalization, rescaling, and error level analysis (ELA) are performed in the pre-processing phase, which can improve accuracy performance. In addition, the proposed pre-processing technique can reduce the overfitting issues in the network model. Then, the image augmentation is conquered to maximize the size of the dataset images. Finally, the convolutional Autoencoder based deep learning (DL) technique is proposed to accurately classify the fake image. The MICC-F220 dataset is utilized for experimentation, and the proposed method is processed using the PYTHON platform. The proposed method achieves an overall accuracy of 99.2%, a specificity of 96.5%, a recall of 95.79% and an F1 score of 96.09%. In addition, the proposed method is compared with conventional techniques and proves the system’s efficiency.

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References

  1. Agarwal R, Verma OP (2020) An efficient copy move forgery detection using deep learning feature extraction and matching algorithm. Multimed Tools Appl 79(11):7355–7376

    Article  Google Scholar 

  2. Ayalneh DA, Kim HJ and Choi YS (2014) JPEG copy paste forgery detection using BAG optimized for complex images. In 16th international conference on advanced communication technology, 181–185. IEEE

  3. Bateman P, Ho ATS and Woodward A (2009) Image forensics of digital cameras by analyzing image variations using statistical process control. In 2009 7th international conference on information, Communications and Signal Processing (ICICS), IEEE, 1–5

  4. Bhatti UA, Yuan L, Yu Z, Nawaz SA, Mehmood A, Bhatti MA, Nizamani MM, Xiao S (2021) Predictive data modeling using sp-kNN for risk factor evaluation in urban demographical healthcare data. J Med Imaging Health Inf 11(1):7–14

    Article  Google Scholar 

  5. Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: A survey. Digit Investig 10(3):226–245

    Article  Google Scholar 

  6. Cannas ED, Horváth J, Baireddy S, Bestagini P, Delp EJ and Tubaro S (2022) Panchromatic imagery copy-paste localization through data-driven sensor attribution. In ICASSP 2022-2022 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, Singapore, pp 2889–2893

  7. Chen C, Shi YQ and Su W (2008) A machine learning based scheme for double JPEG compression detection. In 2008 19th international conference on pattern recognition, IEEE, 1-4

  8. Chen J, Kang X, Liu Y, Wang ZJ (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22(11):1849–1853

    Article  Google Scholar 

  9. Cozzolino D, Poggi G, Sansone C, Verdoliva L (2012) A comparative analysis of forgery detection algorithms. In: Joint IAPR international workshops on statistical techniques in pattern recognition (SPR) and structural and syntactic pattern recognition (SSPR). Springer, Berlin, Heidelberg, pp 693–700

    Google Scholar 

  10. Dogonadze N, Obernosterer J, Hou J (2020) Deep face forgery detection. Computer Vision andPattern Recognition. arXiv preprint arXiv:2004.11804, pp 1–5

  11. Hebbar NK, Kunte AS (2021) Image forgery localization using U-net based architecture and error level analysis. In 2021 3rd international conference on advances in computing, communication control and networking (ICAC3N), IEEE, 1992-1996

  12. Horváth J, Montserrat DM, Hao H, Delp EJ (2020) Manipulation detection in satellite images using deep belief networks. In proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, 664-665

  13. Hosny KM, Hamza HM, Lashin NA (2019) Copy-for-duplication forgery detection in colour images using QPCETMs and sub-image approach. IET Image Process 13(9):1437–1446

    Article  Google Scholar 

  14. Huang F, Huang J, Shi YQ (2010) Detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Forensic Secur 5(4):848–856

    Article  Google Scholar 

  15. Jeronymo DC, Borges YCC, Coelho LS (2017) Image forgery detection by semi-automatic wavelet soft-thresholding with error level analysis. Expert Syst Appl 85:348–356

    Article  Google Scholar 

  16. Jothi JN, Letitia S (2020) Tampering detection using hybrid local and global features in wavelet-transformed space with digital images. Soft Comput 24(7):5427–5443

    Article  Google Scholar 

  17. Kakar P, Sudha N (2012) Exposing postprocessed copy–paste forgeries through transform-invariant features. IEEE Trans Inf Forensic Secur 7(3):1018–1028

    Article  Google Scholar 

  18. Kang G-Y, Feng Y-P, Wang R-K, Lu Z-M (2021) Edge and feature points based video intra-frame passive-blind copy-paste forgery detection. J Netw Intel 6(3):637–645

    Google Scholar 

  19. Kashyap A, Parmar RS, Agrawal M, Gupta H (2017) An evaluation of digital image forgery detection approaches. arXiv preprint arXiv:1703.09968

  20. Kirchner M (2008) Fast and reliable re-sampling detection by spectral analysis of fixed linear predictor residue. In proceedings of the 10th ACM workshop on multimedia and security. https://doi.org/10.1145/1411328.1411333

  21. Kisantal M, Wojna Z, Murawski J, Naruniec J, Cho K (2019) Augmentation for small object detection.Computer Vision and Pattern Recognition. arXiv preprint arXiv:1902.07296

  22. Li W, Yuan Y, Yu N (2008) Detecting copy-paste forgery of jpeg image via block artifact grid extraction. In international workshop on local and non-local approximation in image processing, pp 1–6

  23. Li X-h, Zhao Y-q, Liao M, Shih FY, Shi YQ (2012) Passive detection of copy-paste forgery between JPEG images. J Cent South Univ 19(10):2839–2851

    Article  Google Scholar 

  24. Nandanwar L, Shivakumara P, Mondal P, Raghunandan KS, Pal U, Lu T, Lopresti D (2020) Forged text detection in video, scene, and document images. IET Image Process 14(17):4744–4755

    Article  Google Scholar 

  25. Nida N, Irtaza A, Ilyas N (2021) Forged face detection using ELA and deep learning techniques. In 2021 international Bhurban conference on applied sciences and technologies (IBCAST), IEEE, 271-275

  26. Ouyang J, Liu Y and Liao M (2017) Copy-move forgery detection based on deep learning. In 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, 1-5

  27. Rao Y, Ni J (2016) A deep learning approach to detection of splicing and copy-move forgeries in images. In 2016 IEEE international workshop on information forensics and security (WIFS), IEEE , Abu Dhabi, 1–6

  28. Ravan J (2018) Image forgery detection against forensic image digital tampering. In 2018 international conference on computational techniques, Electronics and Mechanical Systems (CTEMS). IEEE, Belgaum, pp 315–321

  29. Rhee KH (2020) Composition of visual feature vector pattern for deep learning in image forensics. IEEE Access 8:188970–188980

    Article  Google Scholar 

  30. Rota P, Sangineto E, Conotter V, Pramerdorfer C (2016) Bad teacher or unruly student: can deep learning say something in image forensics analysis?. In 2016 23rd international conference on pattern recognition (ICPR), IEEE, Cancun, pp 2503–2508

  31. Sari WP, Fahmi H (2021) The effect of error level analysis on the image forgery detection using deep learning. Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control

  32. Selvaraj P, Karuppiah M (2020) Enhanced copy–paste forgery detection in digital images using scale-invariant feature transform. IET Image Process 14(3):462–471

    Article  Google Scholar 

  33. Singh RD, Aggarwal N (2017) Detection and localization of copy-paste forgeries in digital videos. Forensic Sci Int 281:75–91

    Article  Google Scholar 

  34. Stamm MC, Wu M, Liu KJR (2013) Information forensics: an overview of the first decade. IEEE Access 1:167–200

    Article  Google Scholar 

  35. Sudiatmika IBK, Rahman F, Trisno T, Suyoto S (2019) Image forgery detection using error level analysis and deep learning. TELKOMNIKA (Telecommun Comput Electron Control) 17(2):653–659

    Article  Google Scholar 

  36. Tembe AU, Thombre SS (2017) Survey of copy-paste forgery detection in digital image forensic. In 2017 international conference on innovative mechanisms for industry applications (ICIMIA), IEEE, 248-252

  37. Thakur A, Jindal N (2020) Hybrid deep learning and machine learning approach for passive image forensic. IET Image Process 14(10):1952–1959

    Article  Google Scholar 

  38. Uliyan DM, Jalab HA, Wahab AWA, Shivakumara P, Sadeghi S (2016) A novel forged blurred region detection system for image forensic applications. Expert Syst Appl 64:1–10

    Article  Google Scholar 

  39. Wu Q, Wang S, Zhang X (2010) Detection of image region-duplication with rotation and scaling tolerance. In: International conference on computational collective intelligence. Springer, Berlin, Heidelberg, pp 100–108

    Google Scholar 

  40. Yancey RE, Matloff N, Thompson P (2019) Multi-linear faster RCNN with ELA for image tampering detection. arXiv preprint arXiv:1904.08484

  41. Yang Q-C, Huang C-L (2009) Copy-move forgery detection in digital image. In: Pacific-rim conference on multimedia. Springer, Berlin, Heidelberg, pp 816–825

    Google Scholar 

  42. Yang J, Liang Z, Gan Y, Zhong J (2021) A novel copy-move forgery detection algorithm via two-stage filtering. Digital Signal Process 113:103032

    Article  Google Scholar 

  43. Zeeshan Z, Bhatti UA, Memon WH, Ali S, Nawaz SA, Nizamani MM, Mehmood A, Bhatti MA, Shoukat MU (2021) Feature-based multi-criteria recommendation system using a weighted approach with ranking correlation. Intell Data Anal 25(4):1013–1029

    Article  Google Scholar 

  44. Zhan Y, Chen Y, Zhang Q and Kang X (2017) Image forensics based on transfer learning and convolutional neural network. In proceedings of the 5th ACM workshop on information hiding and multimedia security, 165-170

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Authors and Affiliations

  1. Department of Information Technology, Annamalai University, Annamalai Nagar, Tamil Nadu, 608002, India

    N V S K Vijayalakshmi K & J. Sasikala

  2. Department of Computer Science and Engineering, SRM University, Chennai, Tamil Nadu, 603203, India

    C. Shanmuganathan

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  1. N V S K Vijayalakshmi K
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  2. J. Sasikala
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  3. C. Shanmuganathan
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Vijayalakshmi K, N.V.S.K., Sasikala, J. & Shanmuganathan, C. Copy-paste forgery detection using deep learning with error level analysis. Multimed Tools Appl 83, 3425–3449 (2024). https://doi.org/10.1007/s11042-023-15594-5

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