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Digital image forgery: taxonomy, techniques, and tools–a comprehensive study

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Abstract

Editing and manipulating digital images using easily available open-source software along with powerful commercial software has led to one of the serious issues in the area of multimedia forensics. Though most of the users use these applications for fun when once the user tries to change the image content, intending to mislead the whole information transformed by the image such kind of action needs attention over the intention of the users. The digital image plays a significant role in various areas mainly in forensic investigation, science, digital media, intelligence systems, surveillance systems, a court of law, medical imaging, Journalism, and so on that utilize digital images as evidence for findings. Therefore verifying digital image authenticity and integrity is one of the most raising issues and challenges in the area of digital image forgeries. This survey focuses on various existing image tampering methods, a comparison of various techniques used in detecting, commonly applied detection tools that are used in identifying tampering, along with the discussion on existing tampered image datasets and performance metrics considered for evaluation. Further, the paper discusses several challenges and issues. The technical review article is designed to assist future researchers in the field and provide valuable insights.

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References

  • Ahmed B, Gulliver TA (2020) Image splicing detection using mask-RCNN. Signal Image Video Process 14:1035–1042

    Article  Google Scholar 

  • Ardizzone E, Bruno A, Mazzola G (2015) Copy–move forgery detection by matching triangles of keypoints. IEEE Trans Inf Forensics Secur 10(10):2084–2094

    Article  Google Scholar 

  • Bahrami K, Kot AC, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans Inf Forensics Secur 10(5):999–1009

    Article  Google Scholar 

  • Barad ZJ, Goswami MM (2020) Image forgery detection using deep learning: a survey. In: 2020 6th international conference on advanced computing and communication systems (ICACCS), pp. 571–576. IEEE

  • Bay H, Tuytelaars T, Gool LV (2006) Surf: speeded up robust features. In: European conference on computer vision, pp. 404–417. Springer, Berlin, Heidelberg

  • Bayar B, Stamm MC (2016) 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

  • Bi X, Pun C-M, Yuan X-C (2016) Multi-level dense descriptor and hierarchical feature matching for copy–move forgery detection. Inf Sci 345:226–242

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Carvalho T, Faria FA, Pedrini H, Torres RD, Rocha A (2015) Illuminant-based transformed spaces for image forensics. IEEE Trans Inf Forensics Secur 11(4):720–733

    Article  Google Scholar 

  • Chaitra B, Bhaskar Reddy PV (2019) A study on digital image forgery techniques and its detection. In: 2019 International conference on contemporary computing and informatics (IC3I), pp. 127–130. IEEE

  • 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 

  • Choi HY, Jang HU, Kim D, Son J, Mun SM, Choi S, Lee HK (2017) Detecting composite image manipulation based on deep neural networks. In: 2017 international conference on systems, signals and image processing (IWSSIP), pp. 1–5. IEEE

  • Cozzolino D, Poggi G, Verdoliva L (2015) Efficient dense-field copy-move forgery detection. IEEE Trans Inf Forensics Secur 10(11):2284–2297. https://doi.org/10.1109/TIFS.2015.2455334

    Article  Google Scholar 

  • Cozzolino D, Verdoliva L (2016) Single-image splicing localization through autoencoder-based anomaly detection. In: 2016 IEEE international workshop on information forensics and security (WIFS), pp. 1–6. IEEE

  • Cozzolino D, Poggi G, Verdoliva L (2017) Recasting residual-based local descriptors as convolutional neural networks: an application to image forgery detection. In: Proceedings of the 5th ACM workshop on information hiding and multimedia security, pp. 159–164

  • Elaskily MA, Aslan HK, Elshakankiry OA, Faragallah OS, El-Samie FEA, Dessouky MM (2017) Comparative study of copy-move forgery detection techniques. In: 2017 Intl Conf on advanced control circuits systems (ACCS) Systems & 2017 Intl conf on new paradigms in electronics & information technology (PEIT), 2017, pp. 193-203, doi: https://doi.org/10.1109/ACCS-PEIT.2017.8303041.

  • Farid H (2009) Image forgery detection. IEEE Signal Process Mag 26(2):16–25. https://doi.org/10.1109/MSP.2008.931079

    Article  Google Scholar 

  • Ferreira A, Felipussi SC, Alfaro C, Fonseca P, Vargas-Munoz JE, Dos Santos JA, Rocha A (2016) Behavior knowledge space-based fusion for copy–move forgery detection. IEEE Trans Image Process 25(10):4729–4742

    Article  MathSciNet  MATH  Google Scholar 

  • Fridrich AJ, Soukal BD, Lukáš AJ (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop

  • Gill NK, Garg R, Doegar EA (2017) A review paper on digital image forgery detection techniques. In: 2017 8th international conference on computing, communication and networking technologies (ICCCNT), 2017, pp. 1–7, doi: https://doi.org/10.1109/ICCCNT.2017.8203904

  • Han JG, Park TH, Moon YH, Eom IK (2016) Efficient Markov feature extraction method for image splicing detection using maximization and threshold expansion. J Electron Imaging 25(2):023031

    Article  Google Scholar 

  • He X, Guan Q, Tong Y, Zhao X, Yu H (2016) A novel robust image forensics algorithm based on L1-norm estimation. In: International workshop on digital watermarking, pp. 145–158. Springer, Cham

  • Huang D-Y, Huang C-N, Wu-Chih Hu, Chou C-H (2017) Robustness of copy-move forgery detection under high JPEG compression artifacts. Multimed Tools Appl 76(1):1509–1530

    Article  Google Scholar 

  • Jenadeleh M, Moghaddam ME (2016) Blind detection of region duplication forgery using fractal coding and feature matching. J Forensic Sci 61(3):623–636

    Article  Google Scholar 

  • Jinke X, Guangdong Z (2016) Image forgery detection algorithm based on non sampling wavelet transform and Zernike moments. Int J Secur Appl 10(2):27–38

    Google Scholar 

  • Kashyap A, Parmar RS, Agrawal M, Gupta H (2017) An evaluation of digital image forgery detection approaches. arXiv https://arxiv.org/abs/1703.09968

  • Kaur M, Gupta S (2016) A passive blind approach for image splicing detection based on DWT and LBP histograms. In: International symposium on security in computing and communication, pp. 318–327. Springer, Singapore

  • Kaur H, Kaur K (2015) A brief survey of different techniques for detecting copy-move forgery. Int J Adv Res Comput Sci Softw Eng 5(4):875–882

    Google Scholar 

  • Lee J-C (2015) Copy-move image forgery detection based on Gabor magnitude. J vis Commun Image Represent 31:320–334

    Article  MathSciNet  Google Scholar 

  • Lee J-C, Chang C-P, Chen W-K (2015) Detection of copy–move image forgery using histogram of orientated gradients. Inf Sci 321:250–262

    Article  Google Scholar 

  • Li Y, Zhou J (2018) Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Trans Inf Forensics Secur 14(5):1307–1322

    Article  Google Scholar 

  • Li J, Li X, Yang B, Sun X (2014) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518

    Google Scholar 

  • Li Ce, Ma Q, Xiao L, Li M, Zhang A (2017) Image splicing detection based on Markov features in QDCT domain. Neurocomputing 228:29–36

    Article  Google Scholar 

  • Liang Z, Yang G, Ding X, Li L (2015) An efficient forgery detection algorithm for object removal by exemplar-based image inpainting. J vis Commun Image Represent 30:75–85

    Article  Google Scholar 

  • Liu A, Zhao Z, Zhang C, Yuting Su (2019) Smooth filtering identification based on convolutional neural networks. Multimed Tools Appl 78(19):26851–26865

    Article  Google Scholar 

  • Lowe DG (1999) Object recognition from local scale-invariant features. In: Proceedings of the seventh IEEE international conference on computer vision, vol. 2, pp. 1150–1157. IEEE, doi: https://doi.org/10.1109/ICCV.1999.790410

  • Luo W, Zhenhua Qu, Pan F, Huang J (2007) A survey of passive technology for digital image forensics. Front Comput Sci China 1(2):166–179

    Article  Google Scholar 

  • Mahdian B, Saic S (2009) Using noise inconsistencies for blind image forensics. Image vis Comput 27(10):1497–1503

    Article  Google Scholar 

  • Mahmood T, Mehmood Z, Shah M, Saba T (2018) A robust technique for copy-move forgery detection and localization in digital images via stationary wavelet and discrete cosine transform. J vis Commun Image Represent 53:202–214

    Article  Google Scholar 

  • Mankar SK, Gurjar AA (2015) Image forgery types and their detection: a review. Int J Adv Res Comput Sci Softw Eng 5(4):174–178

    Google Scholar 

  • Mariappan G, Satish AR, Reddy PVB, Maram B (2021) Adaptive partitioning-based copy-move image forgery detection using optimal enabled deep neuro-fuzzy network. Comput Intell. https://doi.org/10.1111/coin.12484

    Article  Google Scholar 

  • Meena KB, Tyagi V (2021) A deep learning based method for image splicing detection. J Phys Conf Ser 1714(1):012038. https://doi.org/10.1088/1742-6596/1714/1/012038

    Article  Google Scholar 

  • Mhiripiri NA, Tendai C (eds) (2017) Media law, ethics, and policy in the digital age. IGI Global, Hershey

    Google Scholar 

  • Namdeo A, Vishwakarma A (2013) A survey on copy move image forgery detection using wavelet transform. Int J Sci Res 4(3):876–878

    Google Scholar 

  • Nath VV, Gaharwar GKS, Gaharwar RD (2015) Comprehensive study of different types image forgeries. Int J Sci Technol Manag 4(1):146–151

    Google Scholar 

  • Ng TT, Chang SF, Lin CY, Sun Q (2006) Passive-blind image forensics. In: Multimedia security technologies for digital rights management, pp. 383–412. Academic Press

  • Nowroozi E, Dehghantanha A, Parizi RM, Choo KK (2020) A survey of machine learning techniques in adversarial image forensics. Comput Secur 100:102092

    Article  Google Scholar 

  • Photo Tampering throughout History (2019) Home papers Research, Georgia Tech College https://www.cc.gatech.edu/~beki/cs4001/history.pdf

  • Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions. https://digitalcommons.dartmouth.edu/cs_tr/254/

  • Pun C-M, Yuan X-C, Bi X-L (2015) Image forgery detection using adaptive oversegmentation and feature point matching. IEEE Trans Inf Forensics Secur 10(8):1705–1716

    Article  Google Scholar 

  • Pun C-M, Liu Bo, Yuan X-C (2016) Multi-scale noise estimation for image splicing forgery detection. J vis Commun Image Represent 38:195–206

    Article  Google Scholar 

  • Qureshi MA, Deriche M (2014) A review on copy move image forgery detection techniques. In: 2014 IEEE 11th international multi-conference on systems, signals & devices (SSD14), pp. 1–5. IEEE

  • Qureshi MM, Qureshi MG (2021) Image forgery detection & localization using regularized U-Net. In: Garg D, Wong K, Sarangapani J, Gupta SK (eds) Advanced computing. IACC 2020. Communications in computer and information science, vol 1367. Springer, Singapore

    Google Scholar 

  • 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), pp. 1–6. IEEE

  • Reis G (1999) Digital image integrity. San Jose, CA. https://www.adobe.com/digitalimag/pdfs/digital_image_integrity.pdf

  • Sharma V, Jha S, Bharti RK (2016) Image forgery and it’s detection technique: a review. Int Res J Eng Technol (IRJET) 3(3):756–762

    Google Scholar 

  • Shi Z, Shen X, Kang H, Lv Y (2018) Image manipulation detection and localization based on the dual-domain convolutional neural networks. IEEE Access 6:76437–76453

    Article  Google Scholar 

  • Silva E, Carvalho T, Ferreira A, Rocha A (2015) Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes. J vis Commun Image Represent 29:16–32

    Article  Google Scholar 

  • Soni B, Das PK, Thounaojam DM (2018) CMFD: a detailed review of block based and key feature based techniques in image copy-move forgery detection. IET Image Process 12(2):167–178

    Article  Google Scholar 

  • Tatkare KA, Mane V (2015) Fusion of SIFT and hue moments features for cloning tamper detection. In: 2015 international conference on applied and theoretical computing and communication technology (iCATccT), pp. 409–414. IEEE

  • Teerakanok S, Tetsutaro U (2019) Copy-move forgery detection: a state-of-the-art technical review and analysis. IEEE Access 7:40550–40568

    Article  Google Scholar 

  • Thapaliya A, Elambo Atonge D, Mazzara M, Chakraborty S, Afanasyev I, Ahmad M (2019) Digital image forgery. In: 6th International Young Scientists Conference on Information Technologies, Telecommunications and Control Systems (ITTCS 2019), Innopolis/Yekaterinburg, Russia, 6 December 2019, Vol. 2525. CEUR-WS. org.

  • Thirunavukkarasu V, Satheesh Kumar J, Chae GS, Kishorkumar J (2018) Non-intrusive forensic detection method using DSWT with reduced feature set for copy-move image tampering. Wirel Pers Commun 98(4):3039–3057

    Article  Google Scholar 

  • Tralic D, Grgic S, Sun X, Rosin PL (2016) Combining cellular automata and local binary patterns for copy-move forgery detection. Multimed Tools Appl 75(24):16881–16903

    Article  MATH  Google Scholar 

  • Uliyan DM, Jalab HA, Abdul Wahab AW, Sadeghi S (2016) Image region duplication forgery detection based on angular radial partitioning and Harris key-points. Symmetry 8(7):62

    Article  MathSciNet  Google Scholar 

  • Ulutas G, Muzaffer G (2016) A new copy move forgery detection method resistant to object removal with uniform background forgery. Math Prob Eng. https://doi.org/10.1155/2016/3215162

    Article  Google Scholar 

  • Ustubioglu B, Ulutas G, Ulutas M, Nabiyev VV (2016) Improved copy-move forgery detection based on the CLDs and colour moments. Imaging Sci J 64(4):215–225

    Article  Google Scholar 

  • Walia S, Kumar K (2017) An eagle-eye view of recent digital image forgery detection methods. In: International conference on next generation computing technologies, pp. 469–487. Springer, Singapore

  • Walia S, Krishan K (2019) Digital image forgery detection: a systematic scrutiny. Aust J Forensic Sci 51(5):488–526

    Article  Google Scholar 

  • Wang Q, Zhang R (2016) Double JPEG compression forensics based on a convolutional neural network. EURASIP J Inf Secur 2016(1):1–12

    Google Scholar 

  • Wang X-Y, Li S, Liu Y-N, Niu Y, Yang H-Y, Zhou Z-l (2017) A new keypoint-based copy-move forgery detection for small smooth regions. Multimed Tools Appl 76(22):23353–23382

    Article  Google Scholar 

  • Wang X-Y, Liu Y-N, Huan Xu, Wang P, Yang H-Y (2018) Robust copy–move forgery detection using quaternion exponent moments. Pattern Anal Appl 21(2):451–467

    Article  MathSciNet  Google Scholar 

  • Wang J, Ni Q, Liu G, Luo X, Jha SK (2020) Image splicing detection based on convolutional neural network with weight combination strategy. J Inf Secur Appl 54:102523

    Google Scholar 

  • 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

  • Warif NB, Abd AW, Wahab A, Idris MYI, Salleh R, Othman F (2017) SIFT-symmetry: a robust detection method for copy-move forgery with reflection attack. J vis Commun Image Represent 46:219–232

    Article  Google Scholar 

  • Wo Y, Yang K, Han G, Chen H, Wu W (2017) Copy–move forgery detection based on multi-radius PCET. IET Image Process 11(2):99–108. https://doi.org/10.1049/iet-ipr.2016.0229

    Article  Google Scholar 

  • Wu Y, Abd-Almageed W, Natarajan P (2018) Busternet: detecting copy-move image forgery with source/target localization. In: Proceedings of the European conference on computer vision (ECCV), pp. 168–184

  • Yang F, Li J, Wei Lu, Weng J (2017) Copy-move forgery detection based on hybrid features. Eng Appl Artif Intell 59:73–83

    Article  Google Scholar 

  • Yu L, Han Qi, Niu X (2016a) Feature point-based copy-move forgery detection: covering the non-textured areas. Multimed Tools Appl 75(2):1159–1176

    Article  Google Scholar 

  • Yu J, Zhan Y, Yang J, Kang X (2016b) A multi-purpose image counter-anti-forensic method using convolutional neural networks. In: International workshop on digital watermarking, pp. 3–15. Springer, Cham

  • Zandi M, Mahmoudi-Aznaveh A, Talebpour A (2016) Iterative copy-move forgery detection based on a new interest point detector. IEEE Trans Inf Forensics Secur 11(11):2499–2512

    Article  Google Scholar 

  • Zhan L, Zhu Y, Mo Z (2016) An image splicing detection method based on PCA minimum eigenvalues. J Inf Hiding Multimed Signal Process 7(3):610–619

    Google Scholar 

  • Zhang Y, Li S, Wang S, Zhao X (2015) Identifying image splicing based on local statistical features in DCT and DWT domain. In: The proceedings of the third international conference on communications, signal processing, and systems, pp. 723–731. Springer, Cham

  • Zhang W, Yang Z, Niu S, Wang J (2016a) Detection of copy-move forgery in flat region based on feature enhancement. In: International workshop on digital watermarking, pp. 159–171. Springer, Cham

  • Zhang Y, Goh J, Win LL, Thing VL (2016b) Image region forgery detection: a deep learning approach. SG-CRC, pp. 1–11

  • Zhang Y, Thing VL (2018) A semi-feature learning approach for tampered region localization across multi-format images. Multimed Tools Appl 77(19):25027–25052

    Article  Google Scholar 

  • Zhao X, Wang S, Li S, Li J (2014) Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Trans Circuits Syst Video Technol 25(2):185–199

    Article  Google Scholar 

  • Zheng J, Liu Y, Ren J, Zhu T, Yan Y, Yang H (2016) Fusion of block and keypoints based approaches for effective copy-move image forgery detection. Multidimens Syst Signal Process 27(4):989–1005

    Article  MathSciNet  MATH  Google Scholar 

  • Zheng L, Zhang Y, Thing VL (2019) A survey on image tampering and its detection in real-world photos. J vis Commun Image Represent 58:380–399

    Article  Google Scholar 

  • Zhong J, Gan Y, Xie S (2016) Radon odd radial harmonic Fourier moments in detecting cloned forgery image. Chaos Solitons Fractals 89:115–129

    Article  MATH  Google Scholar 

  • Zhou H, Shen Y, Zhu X, Liu Bo, Zigang Fu, Fan Na (2016) Digital image modification detection using color information and its histograms. Forensic Sci Int 266:379–388

    Article  Google Scholar 

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  1. Department of Computer Science and Engineering, REVA University, Bengaluru, 560064, India

    B. Chaitra & P. V. Bhaskar Reddy

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Chaitra, B., Reddy, P.V.B. Digital image forgery: taxonomy, techniques, and tools–a comprehensive study. Int J Syst Assur Eng Manag 14 (Suppl 1), 18–33 (2023). https://doi.org/10.1007/s13198-022-01829-5

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