Skip to main content
SpringerLink
Log in

Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

The rapid advancement of science and technology has led to the potential for easy manipulation of multimedia content through the use of diverse editing tools. This poses a significant threat to the credibility and integrity of multimedia information. Consequently, substantiating digital images is becoming gradually crucial as digital images hold vital information and are used as essential pieces of evidence in various sectors. The necessity and relevance of digital image forensics have drawn several academics to develop various detection procedures in image forensics. Passive image forgery detection is the foundation of image forensics. Some common passive forgeries that influence the image’s authenticity are image splicing, copy-move, and retouching. In recent times, substantial research effort has been devoted to developing novel approaches for detecting several image forgeries. This study provides an overview of similar research efforts that have been carried out utilizing a well-defined methodology. Our goal is to create an efficient way for image forensics researchers to discover new features of forgeries. This study presents a brief introduction to image forensics, including a historical perspective, taxonomy, and framework of image forgery detection approaches. Various resources useful to academic researchers, such as journals, datasets, websites, and performance parameters are explored and presented. This paper will provide a comprehensive review that will aid researchers in overcoming the numerous challenges experienced in earlier studies. Also, future directions are provided to help scholars in this domain. The purpose of this research is to evaluate passive image forgery detection approaches, therefore benefiting new researchers.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Data Availibility

All data used in this study are included within the manuscript.

Code Availability

Not applicable.

References

  1. Zhang, Z., Wang, C., & Zhou, X. (2018). A survey on passive image copy-move forgery detection. Journal of Information Processing Systems, 14(1), 6–31.

    Google Scholar 

  2. Jindal, N., & Singh, K. (2019). Digital image forensics-gateway to authenticity: Crafted with observations, trends and forecasts. In Handbook of Multimedia Information Security: Techniques and Applications, pp. 681–701. Springer, Cham.

  3. Reith, M., Carr, C., & Gunsch, G. (2002). An examination of digital forensic models. International Journal of Digital Evidence, 1(3), 1–12.

    Google Scholar 

  4. Böhme, R., Freiling, F. C., Gloe, T., & Kirchner, M. (2009). Multimedia forensics is not computer forensics. In International Workshop on Computational Forensics, pp. 90–103. Springer.

  5. Rogers, M. (2003). The role of criminal profiling in the computer forensics process. Computers & Security, 22(4), 292–298.

    Google Scholar 

  6. Sadeghi, S., Dadkhah, S., Jalab, H. A., Mazzola, G., & Uliyan, D. (2018). State of the art in passive digital image forgery detection: copy-move image forgery. Pattern Analysis and Applications, 21(2), 291–306.

    MathSciNet  Google Scholar 

  7. Sabeena, M., & Abraham, L. (2023). Convolutional block attention based network for copy-move image forgery detection. Multimedia Tools and Applications, 83, 2383–2405.

    Google Scholar 

  8. Tahaoglu, G., Ustubioglu, B., Ulutaş, G., Ulutaş, M., & Nabiyev, V. V. (2023). Robust copy-move forgery detection technique against image degradation and geometric distortion attacks. Wireless Personal Communications, 131, 2919–2947.

    Google Scholar 

  9. Jia, S., Xu, Z., Wang, H., Feng, C., & Wang, T. (2018). Coarse-to-fine copy-move forgery detection for video forensics. IEEE Access, 6, 25323–25335.

    Google Scholar 

  10. Imran, M., Ali, Z., Bakhsh, S. T., & Akram, S. (2017). Blind detection of copy-move forgery in digital audio forensics. IEEE Access, 5, 12843–12855.

    Google Scholar 

  11. Su, Z., Li, M., Zhang, G., Wu, Q., Li, M., Zhang, W., & Yao, X. (2023). Robust audio copy-move forgery detection using constant q spectral sketches and ga-svm. IEEE Transactions on Dependable and Secure Computing, 20, 4016–4031.

    Google Scholar 

  12. Raskar, P. S., & Shah, S. K. (2022). VFDHSOG: Copy-move video forgery detection using histogram of second order gradients. Wireless Personal Communications, 122(2), 1617–1654.

    Google Scholar 

  13. Kaur, H., & Jindal, N. (2020). Deep convolutional neural network for graphics forgery detection in video. Wireless Personal Communications, 112(3), 1763–1781.

    Google Scholar 

  14. Thakur, A., & Ranjan, R. (2023). Evaluate the performance of deep CNN algorithm based on parameters and various geometrical attacks. Wireless Personal Communications, 132(4), 2587–2602.

    Google Scholar 

  15. Annam, S., & Singla, A. (2022). Hyperspectral image classification using deep learning model. ECS Transactions, 107(1), 6427.

    Google Scholar 

  16. Farid, H. (2009). Image forgery detection. IEEE Signal Processing Magazine, 26(2), 16–25.

    Google Scholar 

  17. Soni, B., Das, P. K., & Thounaojam, D. M. (2018). CMFD: A detailed review of block based and key feature based techniques in image copy-move forgery detection. IET Image Processing, 12(2), 167–178.

    Google Scholar 

  18. Abd Warif, N. B., Wahab, A. W. A., Idris, M. Y. I., Ramli, R., Salleh, R., Shamshirband, S., & Choo, K.-K.R. (2016). Copy-move forgery detection: Survey, challenges and future directions. Journal of Network and Computer Applications, 75, 259–278.

    Google Scholar 

  19. Teerakanok, S., & Uehara, T. (2019). Copy-move forgery detection: A state-of-the-art technical review and analysis. IEEE Access, 7, 40550–40568.

    Google Scholar 

  20. Birajdar, G. K., & Mankar, V. H. (2013). Digital image forgery detection using passive techniques: A survey. Digital Investigation, 10(3), 226–245.

    Google Scholar 

  21. Walia, S., & Kumar, K. (2019). Digital image forgery detection: A systematic scrutiny. Australian Journal of Forensic Sciences, 51(5), 488–526.

    Google Scholar 

  22. Ahmad, M., & Khursheed, F. (2021). Digital image forgery detection approaches: A review. In Applications of Artificial Intelligence in Engineering, pp. 863–882. Springer.

  23. Gupta, S., Mohan, N., & Kaushal, P. (2021). Passive image forensics using universal techniques: A review. Artificial Intelligence Review, 1–51.

  24. Vinolin, V., & Sucharitha, M. (2021). Hierarchical categorization and review of recent techniques on image forgery detection. The Computer Journal, 64(11), 1692–1704.

    Google Scholar 

  25. Dixit, R., & Naskar, R. (2017). Review, analysis and parameterisation of techniques for copy-move forgery detection in digital images. IET Image Processing, 11(9), 746–759.

    Google Scholar 

  26. Ansari, M. D., Ghrera, S. P., & Tyagi, V. (2014). Pixel-based image forgery detection: A review. IETE Journal of Education, 55(1), 40–46.

    Google Scholar 

  27. Ferreira, W. D., Ferreira, C. B., da Cruz Júnior, G., & Soares, F. (2020). A review of digital image forensics. Computers & Electrical Engineering, 85, 106685.

    Google Scholar 

  28. Al-Azrak, F. M., Elsharkawy, Z. F., Elkorany, A. S., El Banby, G. M., Dessowky, M. I., El-Samie, A., & Fathi, E. (2020). Copy-move forgery detection based on discrete and surf transforms. Wireless Personal Communications, 110(1), 503–530.

    Google Scholar 

  29. Qureshi, M. A., & Deriche, M. (2015). A bibliography of pixel-based blind image forgery detection techniques. Signal Processing: Image Communication, 39, 46–74.

    Google Scholar 

  30. Rai, A. K., & Srivastava, S. (2023). A thorough investigation on image forgery detection. CMES-Computer Modeling in Engineering & Sciences. https://doi.org/10.32604/cmes.2022.020920

    Article  Google Scholar 

  31. Cao, G., Zhao, Y., Ni, R., & Li, X. (2014). Contrast enhancement-based forensics in digital images. IEEE Transactions on Information Forensics and Security, 9(3), 515–525.

    Google Scholar 

  32. Itier, V., Strauss, O., Morel, L., & Puech, W. (2021). Color noise correlation-based splicing detection for image forensics. Multimedia Tools and Applications, 80(9), 13215–13233.

    Google Scholar 

  33. Goel, N., Kaur, S., & Bala, R. (2021). Dual branch convolutional neural network for copy move forgery detection. IET Image Processing, 15(3), 656–665.

    Google Scholar 

  34. Ouyang, J., Liu, Y., & Liao, M. (2019). Robust copy-move forgery detection method using pyramid model and Zernike moments. Multimedia Tools and Applications, 78(8), 10207–10225.

    Google Scholar 

  35. Dhivya, S., Sangeetha, J., & Sudhakar, B. (2020). Copy-move forgery detection using surf feature extraction and SVM supervised learning technique. Soft Computing, 24(19), 14429–14440.

    Google Scholar 

  36. Li, Y., & Zhou, J. (2018). Fast and effective image copy-move forgery detection via hierarchical feature point matching. IEEE Transactions on Information Forensics and Security, 14(5), 1307–1322.

    Google Scholar 

  37. Yang, H.-Y., Qi, S.-R., Niu, Y., Niu, P.-P., & Wang, X.-Y. (2019). Copy-move forgery detection based on adaptive keypoints extraction and matching. Multimedia Tools and Applications, 78(24), 34585–34612.

    Google Scholar 

  38. Popescu, A. C., & Farid, H. (2004). Exposing digital forgeries by detecting duplicated image regions.

  39. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., Del Tongo, L., & Serra, G. (2013). Copy-move forgery detection and localization by means of robust clustering with j-linkage. Signal Processing: Image Communication, 28(6), 659–669.

    Google Scholar 

  40. Yap, P.-T., Jiang, X., & Kot, A. C. (2009). Two-dimensional polar harmonic transforms for invariant image representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(7), 1259–1270.

    Google Scholar 

  41. Christlein, V., Riess, C., Jordan, J., Riess, C., & Angelopoulou, E. (2012). An evaluation of popular copy-move forgery detection approaches. IEEE Transactions on Information Forensics and Security, 7(6), 1841–1854.

    Google Scholar 

  42. Amerini, I., Ballan, L., Caldelli, R., Del Bimbo, A., & Serra, G. (2011). A sift-based forensic method for copy-move attack detection and transformation recovery. IEEE Transactions on Information Forensics and Security, 6(3), 1099–1110.

    Google Scholar 

  43. Cozzolino, D., Poggi, G., & Verdoliva, L. (2015). Efficient dense-field copy-move forgery detection. IEEE Transactions on Information Forensics and Security, 10(11), 2284–2297.

    Google Scholar 

  44. Wen, B., Zhu, Y., Subramanian, R., Ng, T. -T., Shen, X., & Winkler, S. (2016). Coverage-a novel database for copy-move forgery detection. In 2016 IEEE International Conference on Image Processing (ICIP), pp. 161–165. IEEE.

  45. Tralic, D., Zupancic, I., Grgic, S., & Grgic, M. (2013). COMOFOD-new database for copy-move forgery detection. In Proceedings ELMAR-2013, pp. 49–54. IEEE.

  46. 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. Journal of Visual Communication and Image Representation, 29, 16–32.

    Google Scholar 

  47. Dong, J., Wang, W., & Tan, T. (2013). Casia image tampering detection evaluation database. In 2013 IEEE China Summit and International Conference on Signal and Information Processing, pp. 422–426. IEEE.

  48. Hsu, Y. -F., & Chang, S. -F. (2006). Detecting image splicing using geometry invariants and camera characteristics consistency. In 2006 IEEE International Conference on Multimedia and Expo, pp. 549–552. IEEE.

  49. Ng, T.-T., Hsu, J., & Chang, S.-F. (2009). Columbia image splicing detection evaluation dataset. DVMM lab. Columbia Univ CalPhotos Digit Libr.

    Google Scholar 

  50. Cozzolino, D., Gragnaniello, D., & Verdoliva, L. (2014). Image forgery localization through the fusion of camera-based, feature-based and pixel-based techniques. In 2014 IEEE International Conference on Image Processing (ICIP), pp. 5302–5306. IEEE.

  51. De Carvalho, T. J., Riess, C., Angelopoulou, E., Pedrini, H., & de Rezende Rocha, A. (2013). Exposing digital image forgeries by illumination color classification. IEEE Transactions on Information Forensics and Security, 8(7), 1182–1194.

    Google Scholar 

  52. Xie, D., Liang, L., Jin, L., Xu, J., & Li, M. (2015). Scut-fbp: A benchmark dataset for facial beauty perception. In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1821–1826. IEEE.

  53. Castro, M., Ballesteros, D. M., & Renza, D. (2020). A dataset of 1050-tampered color and grayscale images (CG-1050). Data in Brief, 28, 104864.

    Google Scholar 

  54. Darmet, L., Wang, K., & Cayre, F. (2021). Disentangling copy-moved source and target areas. Applied Soft Computing, 109, 107536.

    Google Scholar 

  55. Fridrich, A. J., Soukal, B. D., & Lukáš, A. J. (2003). Detection of copy-move forgery in digital images. In Proceedings of Digital Forensic Research Workshop. CiteSeer.

  56. Li, G., Wu, Q., Tu, D., & Sun, S. (2007). A sorted neighborhood approach for detecting duplicated regions in image forgeries based on dwt and SVD. In 2007 IEEE International Conference on Multimedia and Expo, pp. 1750–1753. IEEE.

  57. Bravo-Solorio, S., & Nandi, A. K. (2009). Passive forensic method for detecting duplicated regions affected by reflection, rotation and scaling. In 2009 17th European Signal Processing Conference, pp. 824–828. IEEE.

  58. Bravo-Solorio, S., & Nandi, A. K. (2011). Exposing duplicated regions affected by reflection, rotation and scaling. In 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1880–1883. IEEE.

  59. Wu, Q., Wang, S., & Zhang, X. (2011). Log-polar based scheme for revealing duplicated regions in digital images. IEEE Signal Processing Letters, 18(10), 559–562.

    Google Scholar 

  60. Muhammad, G., Hussain, M., & Bebis, G. (2012). Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digital Investigation, 9(1), 49–57.

    Google Scholar 

  61. Zhong, J., & Gan, Y. (2016). Detection of copy-move forgery using discrete analytical Fourier-Mellin transform. Nonlinear Dynamics, 84(1), 189–202.

    MathSciNet  Google Scholar 

  62. 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. Journal of Visual Communication and Image Representation, 53, 202–214.

    Google Scholar 

  63. Meena, K. B., & Tyagi, V. (2020). A copy-move image forgery detection technique based on Tetrolet transform. Journal of Information Security and Applications, 52, 102481.

    Google Scholar 

  64. Priyanka, Singh, G., & Singh, K. (2020). An improved block based copy-move forgery detection technique. Multimedia Tools and Applications, 79(19), 13011–13035.

    Google Scholar 

  65. Cao, Y., Gao, T., Fan, L., & Yang, Q. (2012). A robust detection algorithm for copy-move forgery in digital images. Forensic Science International, 214(1–3), 33–43.

    Google Scholar 

  66. Zhong, J., Gan, Y., Young, J., Huang, L., & Lin, P. (2017). A new block-based method for copy move forgery detection under image geometric transforms. Multimedia Tools and Applications, 76(13), 14887–14903.

    Google Scholar 

  67. Wang, H., & Wang, H. (2018). Perceptual hashing-based image copy-move forgery detection. Security and Communication Networks., 2018, 1–11.

    Google Scholar 

  68. Chen, B., Yu, M., Su, Q., Shim, H. J., & Shi, Y.-Q. (2018). Fractional quaternion Zernike moments for robust color image copy-move forgery detection. IEEE Access, 6, 56637–56646.

    Google Scholar 

  69. Hosny, K. M., Hamza, H. M., & Lashin, N. A. (2018). Copy-move forgery detection of duplicated objects using accurate PCET moments and morphological operators. The Imaging Science Journal, 66(6), 330–345.

    Google Scholar 

  70. Meena, K. B., & Tyagi, V. (2019). A copy-move image forgery detection technique based on Gaussian-Hermite moments. Multimedia Tools and Applications, 78(23), 33505–33526.

    Google Scholar 

  71. Nirmal Jothi, J., & Letitia, S. (2020). Tampering detection using hybrid local and global features in wavelet-transformed space with digital images. Soft Computing, 24(7), 5427–5443.

    Google Scholar 

  72. Gani, G., & Qadir, F. (2020). A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata. Journal of Information Security and Applications, 54, 102510.

    Google Scholar 

  73. Al-Qershi, O. M., & Khoo, B. E. (2019). Enhanced block-based copy-move forgery detection using K-means clustering. Multidimensional Systems and Signal Processing, 30(4), 1671–1695.

    Google Scholar 

  74. Kasban, H., & Nassar, S. (2020). An efficient approach for forgery detection in digital images using Hilbert-Huang transform. Applied Soft Computing, 97, 106728.

    Google Scholar 

  75. Ahmed, B., Gulliver, T. A., & alZahir, S. (2020). Blind copy-move forgery detection using SVD and KS test. SN Applied Sciences, 2(8), 1–12.

    Google Scholar 

  76. Babu, S. T., & Rao, C. S. (2021). An optimized technique for copy–move forgery localization using statistical features. ICT Express.

  77. Gani, G., & Qadir, F. (2021). Copy move forgery detection using DCT, PatchMatch and cellular automata. Multimedia Tools and Applications, 80(21), 32219–32243.

    Google Scholar 

  78. Pan, X., & Lyu, S. (2010). Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 5(4), 857–867.

    Google Scholar 

  79. Zhao, J., & Zhao, W. (2013). Passive forensics for region duplication image forgery based on Harris feature points and local binary patterns. Mathematical Problems in Engineering, 2013, 619564.

    Google Scholar 

  80. Jaberi, M., Bebis, G., Hussain, M., & Muhammad, G. (2014). Accurate and robust localization of duplicated region in copy-move image forgery. Machine Vision and Applications, 25(2), 451–475.

    Google Scholar 

  81. Yu, L., Han, Q., & Niu, X. (2016). Feature point-based copy-move forgery detection: Covering the non-textured areas. Multimedia Tools and Applications, 75(2), 1159–1176.

    Google Scholar 

  82. Yang, F., Li, J., Lu, W., & Weng, J. (2017). Copy-move forgery detection based on hybrid features. Engineering Applications of Artificial Intelligence, 59, 73–83.

    Google Scholar 

  83. 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. Multimedia Tools and Applications, 76(22), 23353–23382.

    Google Scholar 

  84. Alberry, H. A., Hegazy, A. A., & Salama, G. I. (2018). A fast sift based method for copy move forgery detection. Future Computing and Informatics Journal, 3(2), 159–165.

    Google Scholar 

  85. Wang, X.-Y., Jiao, L.-X., Wang, X.-B., Yang, H.-Y., & Niu, P.-P. (2018). A new keypoint-based copy-move forgery detection for color image. Applied Intelligence, 48(10), 3630–3652.

    Google Scholar 

  86. Liu, K., Lu, W., Lin, C., Huang, X., Liu, X., Yeung, Y., & Xue, Y. (2019). Copy move forgery detection based on keypoint and patch match. Multimedia Tools and Applications, 78(22), 31387–31413.

    Google Scholar 

  87. Wang, X.-Y., Wang, C., Wang, L., Jiao, L.-X., Yang, H.-Y., & Niu, P.-P. (2020). A fast and high accurate image copy-move forgery detection approach. Multidimensional Systems and Signal Processing, 31(3), 857–883.

    Google Scholar 

  88. Uma, S., & Sathya, P. (2020). Copy-move forgery detection of digital images using football game optimization. Australian Journal of Forensic Sciences, 54, 1–22.

    Google Scholar 

  89. Niu, P., Wang, C., Chen, W., Yang, H., & Wang, X. (2021). Fast and effective keypoint-based image copy-move forgery detection using complex-valued moment invariants. Journal of Visual Communication and Image Representation, 77, 103068.

    Google Scholar 

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

    Google Scholar 

  91. Lyu, Q., Luo, J., Liu, K., Yin, X., Liu, J., & Lu, W. (2021). Copy move forgery detection based on double matching. Journal of Visual Communication and Image Representation, 76, 103057.

    Google Scholar 

  92. Chen, H., Yang, X., & Lyu, Y. (2020). Copy-move forgery detection based on keypoint clustering and similar neighborhood search algorithm. IEEE Access, 8, 36863–36875.

    Google Scholar 

  93. Bilal, M., Habib, H. A., Mehmood, Z., Yousaf, R. M., Saba, T., & Rehman, A. (2021). A robust technique for copy-move forgery detection from small and extremely smooth tampered regions based on the dhe-surf features and mdbscan clustering. Australian Journal of Forensic Sciences, 53(4), 459–482.

    Google Scholar 

  94. Wang, C., Zhang, Z., Li, Q., & Zhou, X. (2019). An image copy-move forgery detection method based on surf and pcet. IEEE Access, 7, 170032–170047.

    Google Scholar 

  95. Prakash, C. S., Panzade, P. P., Om, H., & Maheshkar, S. (2019). Detection of copy-move forgery using AKAZE and SIFT keypoint extraction. Multimedia Tools and Applications, 78(16), 23535–23558.

    Google Scholar 

  96. Wang, C., Zhang, Z., & Zhou, X. (2018). An image copy-move forgery detection scheme based on A-KAZE and SURF features. Symmetry, 10(12), 706.

    Google Scholar 

  97. Ardizzone, E., Bruno, A., & Mazzola, G. (2015). Copy-move forgery detection by matching triangles of keypoints. IEEE Transactions on Information Forensics and Security, 10(10), 2084–2094.

    Google Scholar 

  98. Pun, C.-M., Yuan, X.-C., & Bi, X.-L. (2015). Image forgery detection using adaptive oversegmentation and feature point matching. IEEE transactions on information forensics and security, 10(8), 1705–1716.

    Google Scholar 

  99. 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. Multidimensional Systems and Signal Processing, 27(4), 989–1005.

    MathSciNet  Google Scholar 

  100. Sun, Y., Ni, R., & Zhao, Y. (2018). Nonoverlapping blocks based copy-move forgery detection. Security and Communication Networks, 2018, 1301290.

    Google Scholar 

  101. Ojeniyi, J. A., Adedayo, B. O., Ismaila, I., & Abdulhamid, S. M. (2018). Hybridized technique for copy-move forgery detection using discrete cosine transform and speeded-up robust feature techniques.

  102. Huang, H.-Y., & Ciou, A.-J. (2019). Copy-move forgery detection for image forensics using the superpixel segmentation and the Helmert transformation. EURASIP Journal on Image and Video Processing, 2019(1), 1–16.

    Google Scholar 

  103. Elhaminia, B., Harati, A., & Taherinia, A. (2019). A probabilistic framework for copy-move forgery detection based on Markov random field. Multimedia Tools and Applications, 78(18), 25591–25609.

    Google Scholar 

  104. Liu, Y., Wang, H., Chen, Y., Wu, H., & Wang, H. (2020). A passive forensic scheme for copy-move forgery based on superpixel segmentation and K-means clustering. Multimedia Tools and Applications, 79(1), 477–500.

    Google Scholar 

  105. Niyishaka, P., & Bhagvati, C. (2020). Copy-move forgery detection using image blobs and brisk feature. Multimedia Tools and Applications, 79(35), 26045–26059.

    Google Scholar 

  106. Meena, K. B., & Tyagi, V. (2020). A hybrid copy-move image forgery detection technique based on Fourier-Mellin and scale invariant feature transforms. Multimedia Tools and Applications, 79(11), 8197–8212.

    Google Scholar 

  107. Agarwal, R., & Verma, O. P. (2021). Robust copy-move forgery detection using modified superpixel based FCM clustering with emperor penguin optimization and block feature matching. Evolving Systems, 13, 1–15.

    Google Scholar 

  108. Tinnathi, S., & Sudhavani, G. (2021). An efficient copy move forgery detection using adaptive watershed segmentation with AGSO and hybrid feature extraction. Journal of Visual Communication and Image Representation, 74, 102966.

    Google Scholar 

  109. Tahaoglu, G., Ulutas, G., Ustubioglu, B., & Nabiyev, V. V. (2021). Improved copy move forgery detection method via l* a* b* color space and enhanced localization technique. Multimedia Tools and Applications, 80(15), 23419–23456.

    Google Scholar 

  110. Ng, T.-T., & Chang, S.-F. (2004). A model for image splicing. In 2004 International Conference on Image Processing, 2004. ICIP’04., vol. 2, pp. 1169–1172. IEEE.

  111. Shi, Y.Q., Chen, C., & Chen, W. (2007). A natural image model approach to splicing detection. In Proceedings of the 9th Workshop on Multimedia & Security, pp. 51–62.

  112. Li, X., Jing, T., & Li, X. (2010). Image splicing detection based on moment features and Hilbert-Huang transform. In 2010 IEEE International Conference on Information Theory and Information Security, pp. 1127–1130. IEEE.

  113. He, Z., Lu, W., Sun, W., & Huang, J. (2012). Digital image splicing detection based on Markov features in DCT and dwt domain. Pattern Recognition, 45(12), 4292–4299.

    Google Scholar 

  114. Rao, M. P., & Rajagopalan, A. (2013). Harnessing motion blur to uncover splicing. In 2013 IEEE International Conference on Image Processing, pp. 4507–4511. IEEE.

  115. Muhammad, G., Al-Hammadi, M. H., Hussain, M., & Bebis, G. (2014). Image forgery detection using steerable pyramid transform and local binary pattern. Machine Vision and Applications, 25(4), 985–995.

    Google Scholar 

  116. Pun, C.-M., Liu, B., & Yuan, X.-C. (2016). Multi-scale noise estimation for image splicing forgery detection. Journal of Visual Communication and Image Representation, 38, 195–206.

    Google Scholar 

  117. Zhang, Q., Lu, W., & Weng, J. (2016). Joint image splicing detection in DCT and contourlet transform domain. Journal of Visual Communication and Image Representation, 40, 449–458.

    Google Scholar 

  118. Zhao, X., Wang, S., Li, S., & Li, J. (2014). Passive image-splicing detection by a 2-D noncausal Markov model. IEEE Transactions on Circuits and Systems for Video Technology, 25(2), 185–199.

    Google Scholar 

  119. Chen, B., Qi, X., Sun, X., & Shi, Y.-Q. (2017). Quaternion pseudo-Zernike moments combining both of RGB information and depth information for color image splicing detection. Journal of Visual Communication and Image Representation, 49, 283–290.

    Google Scholar 

  120. El-Alfy, E.-S.M., & Qureshi, M. A. (2017). Robust content authentication of gray and color images using LBP-DCT Markov-based features. Multimedia Tools and Applications, 76(12), 14535–14556.

    Google Scholar 

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

    Google Scholar 

  122. Moghaddasi, Z., Jalab, H. A., & Noor, R. M. (2019). Image splicing forgery detection based on low-dimensional singular value decomposition of discrete cosine transform coefficients. Neural Computing and Applications, 31(11), 7867–7877.

    Google Scholar 

  123. Han, J. G., Park, T. H., Moon, Y. H., & Eom, I. K. (2018). Quantization-based Markov feature extraction method for image splicing detection. Machine Vision and Applications, 29(3), 543–552.

    Google Scholar 

  124. Subramaniam, T., Jalab, H. A., Ibrahim, R. W., & Mohd Noor, N. F. (2019). Improved image splicing forgery detection by combination of conformable focus measures and focus measure operators applied on obtained redundant discrete wavelet transform coefficients. Symmetry, 11(11), 1392.

    Google Scholar 

  125. Jalab, H. A., Subramaniam, T., Ibrahim, R. W., Kahtan, H., & Noor, N. F. M. (2019). New texture descriptor based on modified fractional entropy for digital image splicing forgery detection. Entropy, 21(4), 371.

    MathSciNet  Google Scholar 

  126. Kaur, N., Jindal, N., & Singh, K. (2020). A passive approach for the detection of splicing forgery in digital images. Multimedia Tools and Applications, 79(43), 32037–32063.

    Google Scholar 

  127. Niyishaka, P., & Bhagvati, C. (2021). Image splicing detection technique based on illumination-reflectance model and LBP. Multimedia Tools and Applications, 80(2), 2161–2175.

    Google Scholar 

  128. Alahmadi, A., Hussain, M., Aboalsamh, H., Muhammad, G., Bebis, G., & Mathkour, H. (2017). Passive detection of image forgery using DCT and local binary pattern. Signal, Image and Video Processing, 11(1), 81–88.

    Google Scholar 

  129. Sheng, H., Shen, X., Lyu, Y., Shi, Z., & Ma, S. (2018). Image splicing detection based on Markov features in discrete octonion cosine transform domain. IET Image Processing, 12(10), 1815–1823.

    Google Scholar 

  130. Zhang, Q., Lu, W., Wang, R., & Li, G. (2018). Digital image splicing detection based on Markov features in block dwt domain. Multimedia Tools and Applications, 77(23), 31239–31260.

    Google Scholar 

  131. Pham, N. T., Lee, J.-W., Kwon, G.-R., & Park, C.-S. (2019). Efficient image splicing detection algorithm based on Markov features. Multimedia Tools and Applications, 78(9), 12405–12419.

    Google Scholar 

  132. Jaiswal, A. K., & Srivastava, R. (2020). Time-efficient spliced image analysis using higher-order statistics. Machine Vision and Applications, 31(7), 1–20.

    Google Scholar 

  133. Kanwal, N., Girdhar, A., Kaur, L., & Bhullar, J. S. (2020). Digital image splicing detection technique using optimal threshold based local ternary pattern. Multimedia Tools and Applications, 79(19), 12829–12846.

    Google Scholar 

  134. Siddiqi, M. H., Asghar, K., Draz, U., Ali, A., Alruwaili, M., Alhwaiti, Y., Alanazi, S., & Kamruzzaman, M. (2021). Image splicing-based forgery detection using discrete wavelet transform and edge weighted local binary patterns. Security and Communication Networks, 2021, 1–10.

    Google Scholar 

  135. Stamm, M., & Liu, K. R. (2008). Blind forensics of contrast enhancement in digital images. In 2008 15th IEEE International Conference on Image Processing, pp. 3112–3115. IEEE.

  136. Stamm, M. C., & Liu, K. R. (2010). Forensic estimation and reconstruction of a contrast enhancement mapping. In ICASSP, pp. 1698–1701. CiteSeer.

  137. Cao, G., Zhao, Y., Ni, R., & Kot, A. C. (2011). Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Processing Letters, 18(10), 603–606.

    Google Scholar 

  138. Ding, F., Zhu, G., Yang, J., Xie, J., & Shi, Y.-Q. (2014). Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Processing Letters, 22(3), 327–331.

    Google Scholar 

  139. Zhu, N., Deng, C., & Gao, X. (2017). Image sharpening detection based on multiresolution overshoot artifact analysis. Multimedia Tools and Applications, 76(15), 16563–16580.

    Google Scholar 

  140. Vázquez-Padín, D., Pérez-González, F., & Comesana-Alfaro, P. (2017). A random matrix approach to the forensic analysis of upscaled images. IEEE Transactions on Information Forensics and Security, 12(9), 2115–2130.

    Google Scholar 

  141. Liu, B., Pun, C.-M., & Yuan, X.-C. (2014). Digital image forgery detection using JPEG features and local noise discrepancies. The Scientific World Journal, 2014, 230425.

    Google Scholar 

  142. Prakash, C. S., Kumar, A., Maheshkar, S., & Maheshkar, V. (2018). An integrated method of copy-move and splicing for image forgery detection. Multimedia Tools and Applications, 77(20), 26939–26963.

    Google Scholar 

  143. Jaiprakash, S. P., Desai, M. B., Prakash, C. S., Mistry, V. H., & Radadiya, K. L. (2020). Low dimensional DCT and dwt feature based model for detection of image splicing and copy-move forgery. Multimedia Tools and Applications, 79(39), 29977–30005.

    Google Scholar 

  144. Dua, S., Singh, J., & Parthasarathy, H. (2020). Detection and localization of forgery using statistics of DCT and Fourier components. Signal Processing: Image Communication, 82, 115778.

    Google Scholar 

  145. Pham, N. T., Lee, J.-W., & Park, C.-S. (2020). Structural correlation based method for image forgery classification and localization. Applied Sciences, 10(13), 4458.

    Google Scholar 

  146. Kaur, N., Jindal, N., & Singh, K. (2021). Efficient hybrid passive method for the detection and localization of copy-move and spliced images. Turkish Journal of Electrical Engineering & Computer Sciences, 29(2), 561–582.

    Google Scholar 

  147. Al-Azrak, F. M., Sedik, A., Dessowky, M. I., El Banby, G. M., Khalaf, A. A., Elkorany, A. S., et al. (2020). An efficient method for image forgery detection based on trigonometric transforms and deep learning. Multimedia Tools and Applications, 79(25), 18221–18243.

    Google Scholar 

  148. 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.

  149. Xiao, B., Wei, Y., Bi, X., Li, W., & Ma, J. (2020). Image splicing forgery detection combining coarse to refined convolutional neural network and adaptive clustering. Information Sciences, 511, 172–191.

    MathSciNet  Google Scholar 

  150. Jaiswal, A. K., & Srivastava, R. (2021). Detection of copy-move forgery in digital image using multi-scale, multi-stage deep learning model. Neural Processing Letters, 54(1), 75–100.

    Google Scholar 

  151. Rao, Y., Ni, J., & Zhao, H. (2020). Deep learning local descriptor for image splicing detection and localization. IEEE Access, 8, 25611–25625.

    Google Scholar 

  152. Rodriguez-Ortega, Y., Ballesteros, D. M., & Renza, D. (2021). Copy-move forgery detection (CMFD) using deep learning for image and video forensics. Journal of Imaging, 7(3), 59.

    Google Scholar 

  153. Chen, J., Kang, X., Liu, Y., & Wang, Z. J. (2015). Median filtering forensics based on convolutional neural networks. IEEE Signal Processing Letters, 22(11), 1849–1853.

    Google Scholar 

  154. Liu, Y., Zhu, X., Zhao, X., & Cao, Y. (2019). Adversarial learning for constrained image splicing detection and localization based on atrous convolution. IEEE Transactions on Information Forensics and Security, 14(10), 2551–2566.

    Google Scholar 

  155. Abdalla, Y., Iqbal, M. T., & Shehata, M. (2019). Copy-move forgery detection and localization using a generative adversarial network and convolutional neural-network. Information, 10(9), 286.

    Google Scholar 

  156. Jabeen, S., Khan, U. G., Iqbal, R., Mukherjee, M., & Lloret, J. (2021). A deep multimodal system for provenance filtering with universal forgery detection and localization. Multimedia Tools and Applications, 80(11), 17025–17044.

    Google Scholar 

  157. Elaskily, M. A., Elnemr, H. A., Sedik, A., Dessouky, M. M., El Banby, G. M., Elshakankiry, O. A., Khalaf, A. A., Aslan, H. K., Faragallah, O. S., El-Samie, A., et al. (2020). A novel deep learning framework for copy-moveforgery detection in images. Multimedia Tools and Applications, 79(27), 19167–19192.

    Google Scholar 

  158. Ahmed, B., Gulliver, T. A., & alZahir, S. (2020). Image splicing detection using mask-RCNN. Signal, Image and Video Processing, 14(5), 1035–1042.

    Google Scholar 

  159. Shi, C., Chen, L., Wang, C., Zhou, X., & Qin, Z. (2023). Review of image forensic techniques based on deep learning. Mathematics, 11(14), 3134.

    Google Scholar 

  160. Shukla, D. K., Bansal, A., & Singh, P. (2024). A survey on digital image forensic methods based on blind forgery detection. Multimedia Tools and Applications, 1–32.

Download references

Funding

It is declared by the authors that they did not receive any grants, funds, or other forms of assistance during the preparation of this manuscript.

Author information

Author notes

  1. Navneet Kaur, Neeru Jindal and Kulbir Singh have contributed equally to this work.

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Navneet Kaur, Neeru Jindal & Kulbir Singh

  2. Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, Solan, India

    Navneet Kaur

Authors
  1. Navneet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neeru Jindal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kulbir Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study’s conception and design were jointly contributed to by all authors. NK, NJ, and KS were responsible for the completion of material preparation, data capture, and analysis. All authors provided feedback on earlier versions of the manuscript, with NK composing the initial draft. The conclusive manuscript was reviewed and endorsed by all authors.

Corresponding author

Correspondence to Kulbir Singh.

Ethics declarations

Conflict of interest

There are no pertinent financial or non-financial interests to disclose by the authors.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kaur, N., Jindal, N. & Singh, K. Passive Image Forgery Detection Techniques: A Review, Challenges, and Future Directions. Wireless Pers Commun 134, 1491–1529 (2024). https://doi.org/10.1007/s11277-024-10959-x

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-024-10959-x

Keywords

Search

Navigation