Abstract
Forgeries have recently become more prevalent in the society as a result of recent improvements in media generation technologies. In real-time, modern technology allows for the creation of a forged version of a single image obtained from a social network. Forgery detection algorithms have been created for a variety of areas; however they quickly become obsolete as new attack types exist. This paper presents a unique image forgery detection strategy based on deep learning algorithms. The proposed approach employs a convolutional neural network (CNN) to produce histogram representations from input RGB color images, which are then utilized to detect image forgeries. With the image separation method and copy-move detection applications in mind, the proposed CNN is combined with an intelligent approach and histogram mapping. It is used to detect fake or true images at the initial stage of our proposed work. Besides, it is specially designed for performing feature extraction in image layer separation with the help of CNN model. To capture both geographical and histogram information and the likelihood of presence at the same time, we use vectors in our dynamic capsule networks to detect the forgery kernels from reference images. The proposed research work integrates the intelligence with a feature engineering approach in an efficient manner. They are well-known and efficient in the identification of forged images. The performance metrics such as accuracy, recall, precision, and half total error rate (HTER) are computed and tabulated with the graph plot.
References
Silva, Jesús, Noel Varela, Fabio E. Mendoza-Palechor, and Omar Bonerge Pineda Lezama. "Deep learning of robust representations for multi-instance and multi-label image classification." In International Conference on Image Processing and Capsule Networks, pp. 169-178. Springer, Cham, 2020.
Kumar, T. Senthil. "Study of Retail Applications with Virtual and Augmented Reality Technologies." Journal of Innovative Image Processing (JIIP) 3, no. 02 (2021): 144-156.
Basha, S., Dubey, S. R., Pulabaigari, V. & Mukherjee, S. Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing. 378, 112–119 (2020).
Adam, Edriss Eisa Babikir. "Survey on Medical Imaging of Electrical Impedance Tomography (EIT) by Variable Current Pattern Methods." Journal of ISMAC 3, no. 02 (2021): 82-95.
Janeera, D. A., and S. Sasipriya. "A Brain Computer Interface Based Patient Observation and Indoor Locating System with Capsule Network Algorithm." In International Conference on Image Processing and Capsule Networks, pp. 258-268. Springer, Cham, 2020.
Wei, Q., Jiang, Y. & Chen, J. Machine-learning solver for modified diffusion equations. Phys. Rev. E 98, 053304 (2018).
Kurup, R. Vimal, M. A. Anupama, R. Vinayakumar, V. Sowmya, and K. P. Soman. "Capsule network for plant disease and plant species classification." In International conference on computational vision and bio inspired computing, pp. 413-421. Springer, Cham, 2019.
Z. Wu, R. K. Das, J. Yang, and H. Li, “Light convolutional neural network with feature genuinization for detection of synthetic speech attacks,” in Inter speech 2020, 2020.
Koppar, Anant, Siddharth Kailasam, M. Varun, and Iresh Hiremath. "Pediatric Bone Age Detection Using Capsule Network." In Inventive Computation and Information Technologies, pp. 405-420. Springer, Singapore, 2021.
Y. Yang, H. Wang, H. Dinkel, Z. Chen, S. Wang, Y. Qian, and K. Yu, “The SJTU Robust Anti-Spoofing System for the ASVspoof 2019 Challenge,” in Inter speech 2019, 2019, pp. 1038–1042.
Senthilkumar, D., C. Akshayaa, and D. George Washington. "Efficient Deep Learning Approach for Multi-label Semantic Scene Classification." In International Conference on Image Processing and Capsule Networks, pp. 397-410. Springer, Cham, 2020.
D. Cozzolino, G. Poggi and L. Verdoliva, “Splicebuster: A new blind image splicing detector,” in IEEE Workshop on Information Forensics and Security (WIFS), 2015, Rome, 2015, pp. 1–6.
Manoharan, J. Samuel. "Capsule Network Algorithm for Performance Optimization of Text Classification." Journal of Soft Computing Paradigm (JSCP) 3, no. 01 (2021): 1-9.
Yohanandan, S. A., Dyer, A. G., Tao, D. & Song, A. Saliencypreservation in low-resolution grayscale images. Eur. Conf. Comput. Vis. (ECCV). 6, 235–251 (2018).
Balasubramaniam, Vivekanadam. "Artificial Intelligence Algorithm with SVM Classification using Dermascopic Images for Melanoma Diagnosis." Journal of Artificial Intelligence and Capsule Networks 3, no. 1: 34-42.
A. Gomez-Alanis, A. M. Peinado, J. A. Gonzalez, and A. M. Gomez, “A light convolutional GRU-RNN deep feature extractor for ASV spoofing detection,” in Proc. Interspeech 2019, 2019, pp. 1068–1072.
Adam, Edriss Eisa Babikir, and A. Sathesh. "Construction of Accurate Crack Identification on Concrete Structure using Hybrid Deep Learning Approach." Journal of Innovative Image Processing (JIIP) 3, no. 02 (2021): 85-99.
Y. Q. Shi, C. Chen, and W. Chen, “A natural image model approach to splicing detection,” in Proceedings of the 9th workshop on Multimedia & security. (MM & Sec), Dallas, TX, USA, 2007, pp. 51–62.
Joon Son Chung, Amir Jamaludin, and Andrew Zisserman, “You said that?,” arXiv preprint arXiv:1705.02966, 2017.
Supasorn Suwajanakorn, Steven M Seitz, and Ira Kemelmacher-Shlizerman, “Synthesizing obama: learning lip sync from audio,” ACM TOG, 2017.
Wonjun Kim, Sungjoo Suh, and Jae-Joon Han, “Face liveness detection from a single image via diffusion speed model,” IEEE TIP, 2015.
Jianwei Yang, Zhen Lei, and Stan Z Li, “Learn convolutional neural network for face anti-spoofing,” arXiv preprint arXiv:1408.5601, 2014.
David Menotti, Giovani Chiachia, Allan Pinto, William Robson Schwartz, Helio Pedrini, Alexandre Xavier Falcao, and Anderson Rocha, “Deep representations for iris, face, and fingerprint spoofing detection,” IEEE TIFS, 2015.
Aziz Alotaibi and Ausif Mahmood, “Deep face liveness detection based on nonlinear diffusion using convolution neural network,” Signal, Image and Video Processing, 2017.
X. Zhao, S. Wang, S. Li and J. Li, “Passive Image-Splicing Detection by a 2-D Noncausal Markov Model,” in IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 2, pp. 185–199, Feb. 2015.
S. Lyu, X. Pan, and X. Zhang, “Exposing Region Splicing Forgeries with Blind Local Noise Estimation,” International Journal of Computer Vision, vol. 110, no. 2, pp. 202–221, 2014.
G. Muhammad, M. Al–Hammadi, M. Hussain, G. Bebis, “Image forgery detection using steerable pyramid transform and local binary pattern,” Machine Vision and Applications, pp. 1–11, 2013.
S. Sabour, N. Frosst, and G. E. Hinton, “Dynamic routing between capsules,” in Advances in Neural Information Processing Systems 30. Curran Associates, Inc., 2017, pp. 3856–3866.
Aziz Alotaibi and Ausif Mahmood, “Deep face liveness detection based on nonlinear diffusion using convolution neural network,” Signal, Image and Video Processing, 2017.
Dhaya, R. "Hybrid Machine Learning Approach to Detect the Changes in SAR Images for Salvation of Spectral Constriction Problem." Journal of Innovative Image Processing (JIIP) 3, no. 02 (2021): 118-130.
Raj, Jennifer S. "Security Enhanced Blockchain based Unmanned Aerial Vehicle Health Monitoring System." Journal of ISMAC 3, no. 02 (2021): 121-131.
Chen, Joy Iong Zong, and Joy Iong Zong. "Automatic Vehicle License Plate Detection using K-Means Clustering Algorithm and CNN." Journal of Electrical Engineering and Automation 3, no. 1 (2021): 15-23.
Smys, S., and Wang Haoxiang. "Naïve Bayes and Entropy based Analysis and Classification of Humans and Chat Bots." Journal of ISMAC 3, no. 01 (2021): 40-49.
Ranganathan, G. "A Study to Find Facts Behind Preprocessing on Deep Learning Algorithms." Journal of Innovative Image Processing (JIIP) 3, no. 01 (2021): 66-74.