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.
Similar content being viewed by others
Data Availibility
All data used in this study are included within the manuscript.
Code Availability
Not applicable.
References
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.
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.
Reith, M., Carr, C., & Gunsch, G. (2002). An examination of digital forensic models. International Journal of Digital Evidence, 1(3), 1–12.
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.
Rogers, M. (2003). The role of criminal profiling in the computer forensics process. Computers & Security, 22(4), 292–298.
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.
Sabeena, M., & Abraham, L. (2023). Convolutional block attention based network for copy-move image forgery detection. Multimedia Tools and Applications, 83, 2383–2405.
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.
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.
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.
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.
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.
Kaur, H., & Jindal, N. (2020). Deep convolutional neural network for graphics forgery detection in video. Wireless Personal Communications, 112(3), 1763–1781.
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.
Annam, S., & Singla, A. (2022). Hyperspectral image classification using deep learning model. ECS Transactions, 107(1), 6427.
Farid, H. (2009). Image forgery detection. IEEE Signal Processing Magazine, 26(2), 16–25.
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.
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.
Teerakanok, S., & Uehara, T. (2019). Copy-move forgery detection: A state-of-the-art technical review and analysis. IEEE Access, 7, 40550–40568.
Birajdar, G. K., & Mankar, V. H. (2013). Digital image forgery detection using passive techniques: A survey. Digital Investigation, 10(3), 226–245.
Walia, S., & Kumar, K. (2019). Digital image forgery detection: A systematic scrutiny. Australian Journal of Forensic Sciences, 51(5), 488–526.
Ahmad, M., & Khursheed, F. (2021). Digital image forgery detection approaches: A review. In Applications of Artificial Intelligence in Engineering, pp. 863–882. Springer.
Gupta, S., Mohan, N., & Kaushal, P. (2021). Passive image forensics using universal techniques: A review. Artificial Intelligence Review, 1–51.
Vinolin, V., & Sucharitha, M. (2021). Hierarchical categorization and review of recent techniques on image forgery detection. The Computer Journal, 64(11), 1692–1704.
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.
Ansari, M. D., Ghrera, S. P., & Tyagi, V. (2014). Pixel-based image forgery detection: A review. IETE Journal of Education, 55(1), 40–46.
Ferreira, W. D., Ferreira, C. B., da Cruz Júnior, G., & Soares, F. (2020). A review of digital image forensics. Computers & Electrical Engineering, 85, 106685.
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.
Qureshi, M. A., & Deriche, M. (2015). A bibliography of pixel-based blind image forgery detection techniques. Signal Processing: Image Communication, 39, 46–74.
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
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.
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.
Goel, N., Kaur, S., & Bala, R. (2021). Dual branch convolutional neural network for copy move forgery detection. IET Image Processing, 15(3), 656–665.
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.
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.
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.
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.
Popescu, A. C., & Farid, H. (2004). Exposing digital forgeries by detecting duplicated image regions.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Ng, T.-T., Hsu, J., & Chang, S.-F. (2009). Columbia image splicing detection evaluation dataset. DVMM lab. Columbia Univ CalPhotos Digit Libr.
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.
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.
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.
Castro, M., Ballesteros, D. M., & Renza, D. (2020). A dataset of 1050-tampered color and grayscale images (CG-1050). Data in Brief, 28, 104864.
Darmet, L., Wang, K., & Cayre, F. (2021). Disentangling copy-moved source and target areas. Applied Soft Computing, 109, 107536.
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.
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.
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.
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.
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.
Muhammad, G., Hussain, M., & Bebis, G. (2012). Passive copy move image forgery detection using undecimated dyadic wavelet transform. Digital Investigation, 9(1), 49–57.
Zhong, J., & Gan, Y. (2016). Detection of copy-move forgery using discrete analytical Fourier-Mellin transform. Nonlinear Dynamics, 84(1), 189–202.
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.
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.
Priyanka, Singh, G., & Singh, K. (2020). An improved block based copy-move forgery detection technique. Multimedia Tools and Applications, 79(19), 13011–13035.
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.
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.
Wang, H., & Wang, H. (2018). Perceptual hashing-based image copy-move forgery detection. Security and Communication Networks., 2018, 1–11.
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.
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.
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.
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.
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.
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.
Kasban, H., & Nassar, S. (2020). An efficient approach for forgery detection in digital images using Hilbert-Huang transform. Applied Soft Computing, 97, 106728.
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.
Babu, S. T., & Rao, C. S. (2021). An optimized technique for copy–move forgery localization using statistical features. ICT Express.
Gani, G., & Qadir, F. (2021). Copy move forgery detection using DCT, PatchMatch and cellular automata. Multimedia Tools and Applications, 80(21), 32219–32243.
Pan, X., & Lyu, S. (2010). Region duplication detection using image feature matching. IEEE Transactions on Information Forensics and Security, 5(4), 857–867.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Uma, S., & Sathya, P. (2020). Copy-move forgery detection of digital images using football game optimization. Australian Journal of Forensic Sciences, 54, 1–22.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Sun, Y., Ni, R., & Zhao, Y. (2018). Nonoverlapping blocks based copy-move forgery detection. Security and Communication Networks, 2018, 1301290.
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.
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.
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.
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.
Niyishaka, P., & Bhagvati, C. (2020). Copy-move forgery detection using image blobs and brisk feature. Multimedia Tools and Applications, 79(35), 26045–26059.
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.
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.
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.
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.
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.
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.
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.
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.
Rao, M. P., & Rajagopalan, A. (2013). Harnessing motion blur to uncover splicing. In 2013 IEEE International Conference on Image Processing, pp. 4507–4511. IEEE.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Niyishaka, P., & Bhagvati, C. (2021). Image splicing detection technique based on illumination-reflectance model and LBP. Multimedia Tools and Applications, 80(2), 2161–2175.
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.
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.
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.
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.
Jaiswal, A. K., & Srivastava, R. (2020). Time-efficient spliced image analysis using higher-order statistics. Machine Vision and Applications, 31(7), 1–20.
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.
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.
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.
Stamm, M. C., & Liu, K. R. (2010). Forensic estimation and reconstruction of a contrast enhancement mapping. In ICASSP, pp. 1698–1701. CiteSeer.
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.
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.
Zhu, N., Deng, C., & Gao, X. (2017). Image sharpening detection based on multiresolution overshoot artifact analysis. Multimedia Tools and Applications, 76(15), 16563–16580.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Rao, Y., Ni, J., & Zhao, H. (2020). Deep learning local descriptor for image splicing detection and localization. IEEE Access, 8, 25611–25625.
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.
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.
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.
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.
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.
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.
Ahmed, B., Gulliver, T. A., & alZahir, S. (2020). Image splicing detection using mask-RCNN. Signal, Image and Video Processing, 14(5), 1035–1042.
Shi, C., Chen, L., Wang, C., Zhou, X., & Qin, Z. (2023). Review of image forensic techniques based on deep learning. Mathematics, 11(14), 3134.
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.
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
Authors and Affiliations
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
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.
About this article
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
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-024-10959-x