Volume 18 • Issue 1 • PP: 103-113 • 2026
Deep Learning Techniques For Image Splicing Detection: A Systematic Review
View PDF Abstract
Currently, images stand for a highly common form of communication, whether through teleconferencing, mobile communication or social media. The identification of counterfeit images is intrinsic because it is crucial that the images used for communication be genuine and original. Images are fabricated referring to the fact that it is challenging to set the difference between a tampered image and the real image. This refers notably to the myriad technological, moral, and judicial implications connected with advanced image editing software. The majority of handcrafted traits are used in traditional approaches for detecting image counterfeiting. The problem with many of the image tampering detection methods now in use resides in the fact that they are confined to identifying particular types of alteration by looking for particular features in the images. Image tampering is currently recognized through deep learning techniques. These methods proved to be promising and worthwhile as they perform better than traditional ones since they can extract complex components from images. As far as this research paper is concerned, we provide a thorough review of deep learning-based methods for detecting splicing images, along with the pertinent results of our survey in the form of findings and analysis.
Keywords
References
[1] Y. Rao and J. Ni, "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, 2016.
[2] C.-S. Park, C. Kim, J. Lee, and G.-R. Kwon, "Rotation and Scale Invariant Upsampled Log-Polar Fourier Descriptor for Copy-Move Forgery Detection," Multimedia Tools and Applications, vol. 75, no. 23, pp. 16577–16595, Dec. 2016.
[3] G. K. Birajdar and V. H. Mankar, "Digital Image Forgery Detection Using Passive Techniques: A Survey," Digital Investigation, vol. 10, no. 3, pp. 226–245, 2013. doi: 10.1016/j.diin.2013.04.007.
[4] A. H. Saber, M. A. Khan, and B. G. Mejbel, "A Survey on Image Forgery Detection Using Different Forensic Approaches," Advances in Science, Technology and Engineering Systems, vol. 5, no. 3, pp. 361–370, 2020. doi: 10.25046/AJ050347.
[5] N. T. Pham and C.-S. Park, "Toward Deep-Learning-Based Methods in Image Forgery Detection: A Survey," IEEE Access, vol. 11, pp. 11224-11237, 2023.
[6] C. N. Bharti and P. Tandel, "A Survey of Image Forgery Detection Techniques," in Proc. 2016 IEEE Int. Conf. Wireless Commun. Signal Process. Networking (WISPNet), pp. 877–881, 2016. doi: 10.1109/WISPNet.2016.7566257.
[7] M. D. Ansari, S. P. Ghrera, and V. Tyagi, "Pixel-Based Image Forgery Detection: A Review," IETE Journal of Education, vol. 55, no. 1, pp. 40–46, 2014. doi: 10.1080/09747338.2014.921415.
[8] K. D. Kadam, S. Ahirrao, K. Kotecha, and S. Sahu, "Detection and Localization of Multiple Image Splicing Using MobileNet V1," IEEE Access, vol. 9, pp. 162499–162519, 2021. doi: 10.1109/ACCESS.2021.3130342.
[9] S. Nath and R. Naskar, "Automated Image Splicing Detection Using Deep CNN-Learned Features and ANN-Based Classifier," Signal, Image and Video Processing, vol. 15, no. 7, pp. 1601–1608, 2021. doi: 10.1007/S11760-021-01895-5.
[10] J. Dong, W. Wang, and T. Tan, "CASIA Image Tampering Detection Evaluation Database," in Proceedings of the 2013 IEEE China Summit and International Conference on Signal and Information Processing, pp. 422–426, Beijing, China, July 2013.
[11] Y.-F. Hsu and S.-F. Chang, "Detecting Image Splicing Using Geometry Invariants and Camera Characteristics Consistency," in Proceedings of the International Conference on Multimedia and Expo (ICME), Toronto, Canada, July 2006.
[12] K. D. Kadam, S. Ahirrao, and K. Kotecha, "Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing," Data, vol. 6, no. 10, p. 102, 2021.
[13] A. R. H. Khayeat, A. A. Al-Moadhen, and M. R. H. Khayyat, "Splicing Detection in Color Image Based on Deep Learning of Wavelet Decomposed Image," AIP Conference Proceedings, vol. 2290, no. December, 2020. doi: 10.1063/5.0027442.
[14] B. Ahmed, T. A. Gulliver, and S. Alzahir, "Image Splicing Detection Using Mask-RCNN," Springer, 2020.
[15] J. Wang, Q. Ni, G. Liu, X. Luo, and S. K. Jha, "Image Splicing Detection Based on Convolutional Neural Network with Weight Combination Strategy," Journal of Information Security and Applications, vol. 54, p. 102523, 2020. doi: 10.1016/J.JISA.2020.102523.
[16] X. Bi, Y. Wei, B. Xiao, and W. Li, "RRU-Net: The Ringed Residual U-Net for Image Splicing Forgery Detection," IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2019-June, pp. 30–39, 2019. doi: 10.1109/CVPRW.2019.00010.
[17] E. I. Abd El-Latif, A. Taha, and H. H. Zayed, "A Passive Approach for Detecting Image Splicing Based on Deep Learning and Wavelet Transform," Arabian Journal for Science and Engineering, vol. 45, no. 4, pp. 3379–3386, 2020. doi: 10.1007/S13369-020-04401-0.
[18] K. B. Meena and V. Tyagi, "A Deep Learning Based Method for Image Splicing Detection," Journal of Physics: Conference Series, vol. 1714, no. 1, 2021. doi: 10.1088/1742-6596/1714/1/012038.
[19] B. Xiao, Y. Wei, X. Bi, W. Li, and J. Ma, "Image Splicing Forgery Detection Combining Coarse to Refined Convolutional Neural Network and Adaptive Clustering," Information Sciences, vol. 511, pp. 172–191, 2020. doi: 10.1016/J.INS.2019.09.038.
[20] H. Ding, L. Chen, Q. Tao, Z. Fu, L. Dong, and X. Cui, "DCU-Net: A Dual-Channel U-Shaped Network for Image Splicing Forgery Detection," Neural Computing and Applications, vol. 4, 2021. doi: 10.1007/S00521-021-06329-4.
[21] Z. Li, Q. You, and J. Sun, "A Novel Deep Learning Architecture with Multi-Scale Guided Learning for Image Splicing Localization," Electronics, vol. 11, no. 10, p. 1607, 2022.
[22] T. Muniappan, N. B. Abd Warif, A. Ismail, and N. A. Mat Abir, "An Evaluation of Convolutional Neural Network (CNN) Model for Copy-Move and Splicing Forgery Detection," International Journal of Intelligent Systems and Applications in Engineering, vol. 11, no. 2, pp. 730-740, 2023.
[23] X. Shi, P. Li, H. Wu, Q. Chen, and H. Zhu, "A Lightweight Image Splicing Tampering Localization Method Based on MobileNetV2 and SRM," IET Image Processing, 2023.
[24] H. L. Nguyen and K. T. Huynh, "A Deep Learning Model for Splicing Image Detection," Rev Journal on Electronics and Communications, vol. 13, no. 3-4, 2024.
Cite This Article
Choose your preferred format