Hybrid Dense Net Based Segmentation Framework for Automated Forgery Detection: Analyzing Copy-Move and Image Splicing Techniques
Keywords:
Hybrid Dense Net Based Segmentation, Automated Forgery Recognition, Copy-Move, Image Splicing, Deep Learning, Digital Image ForensicsAbstract
Protecting information from modification is the biggest challenge in current digital world. To cope up with this a unique hybrid dense net-based segmentation framework for automated forgery recognition is designed. Proposed methodology place a particular emphasis on the investigation of copy-move and image-splicing techniques. When analyzing digital images, system looks for signs of tampering by employing a hybrid approach that combines deep learning with a dense network (DenseNet121) structure. In this paper, authors used a two-stage approach, beginning with coarse-grained segmentation using a modified version of the U-Net structure, and then moving on to fine-grained segmentation using a hybrid dense net. Suggested system is successful for detecting copy-move and image splicing forgery, exceeding DCT & DWT Based forgery detection and CNN-CovLSTM approaches achieves less accuracy then our proposed model, according to extensive testing carried out on a diverse dataset. Notably, when it comes to recognizing challenging copy-move along with frauds, proposed method can obtain far greater rates of accuracy, precision, and recall than prior methods have been capable to achieve. The proposed model achieves an accuracy of 98% for the CASIA-2 dataset a precision of 92% and an F1 score of 90%. For the MICC-F2000 dataset, the proposed model achieves an accuracy of 99%, a precision of 98%, and an impressive F1 score of 99%.
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Xiao, B. Wei, Y. Bi, X. Li, W. Ma, J., “Image splicing forgery detection combining coarse to the refined convolutional neural network and adaptive clustering” Information Sciences 2020, 511, 172–191.
Wu, Y. Abd Almageed, W. Natarajan P, “ManTra-Net: Manipulation Tracing Network for Detection and Localization of Image Forgeries with Anomalous Features” In Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15–20 June 2019; pp. 9535–9544.
Li X, Jing T, Li X., “Image splicing detection based on moment features and Hilbert-Huang Transform” In IEEE international conference on information theory and information security (ICITIS), 2010; Beijing, China; 1127–1130
Castillo Camacho, I. Wang, K, “A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics” J. Imaging 2021, 7, 69.
Hsu J, Yu F. “Image tampering detection for forensics applications, Citeseer”, 2009.
Liu G, Wang J, Lian S, Wang Z, “A passive image authentication scheme for detecting region-duplication forgery with rotation” Journal of Network Computing & Application 2011; 34:1557–1565.
Mushtaq S, Ajaz H, “Novel method for image splicing detection” In International conference on advances in computing, communications, and informatics (ICACCI); 2014; Delhi, India; 2398–2403.
Krishnaraj, N., B. Sivakumar, Ramya Kuppusamy, Yuvaraja Teekaraman, and Amruth Ramesh Thelkar, "Design of automated deep learning-based fusion model for copy-move image forgery detection." Computational Intelligence and Neuroscience 2022 (2022).
Koul, Saboor, Munish Kumar, Surinder Singh Khurana, Faisel Mushtaq, and Krishan Kumar, "An efficient approach for copy-move image forgery detection using convolution neural network." Multimedia Tools and Applications 81, no. 8 (2022): 11259-11277.
Pawar, Shraddha, Gaurangi Pradhan, Bhavin Goswami, and Sonali Bhutad, "Identifying fake images through cnn based classification using fidac." In 2022 International Conference on Intelligent Controller and Computing for Smart Power (ICICCSP), pp. 01-06. IEEE, 2022.
Elaskily, Mohamed A., Monagi H. Alkinani, Ahmed Sedik, and Mohamed M. Dessouky, "Deep learning based algorithm (ConvLSTM) for copy move forgery detection." Journal of Intelligent & Fuzzy Systems 40, no. 3 (2021): 4385-4405.
Agrawal, Prateek, Deepak Chaudhary, Vishu Madaan, Anatoliy Zabrovskiy, Radu Prodan, Dragi Kimovski, and Christian Timmerer, "Automated bank cheque verification using image processing and deep learning methods." Multimedia Tools and Applications 80 (2021): 5319-5350.
Hebbar, Nagaveni K., and Ashwini S. Kunte, "Image forgery localization using U-net based architecture and error level analysis." In 2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), pp. 1992-1996. IEEE, 2021.
Abhishek, and Neeru Jindal, "Copy move and splicing forgery detection using deep convolution neural network, and semantic segmentation." Multimedia Tools and Applications 80 (2021): 3571-3599.
Saber, Akram Hatem, Mohd Ayyub Khan, and Basim Galeb Mejbel, "A survey on image forgery detection using different forensic approaches." Adv Sci Technol Eng Syst J 5, no. 3 (2020): 361-370.
Gani, Gulnawaz, and Fasel Qadir, "A robust copy-move forgery detection technique based on discrete cosine transform and cellular automata." Journal of Information Security and Applications 54 (2020): 102510.
Jaiprakash, Sahani Pooja, Madhavi B. Desai, Choudhary Shyam Prakash, Vipul H. Mistry, and Kishankumar Lalajibhai Radadiya, "Low dimensional DCT and DWT feature based model for detection of image splicing and copy-move forgery." Multimedia Tools and Applications 79 (2020): 29977-30005.
Bappy, Jawadul H., Cody Simons, Lakshmanan Nataraj, B. S. Manjunath, and Amit K. Roy-Chowdhury, "Hybrid lstm and encoder–decoder architecture for detection of image forgeries." IEEE Transactions on Image Processing 28, no. 7 (2019): 3286-3300.
Wu, Yue, Wael Abd-Almageed, and Prem Natarajan, "Image copy-move forgery detection via an end-to-end deep neural network." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 1907-1915. IEEE, 2018.
Yao, Ye, Yunqing Shi, Shaowei Weng, and Bo Guan, "Deep learning for detection of object-based forgery in advanced video." Symmetry 10, no. 1 (2017): 3.
Anshul. K. Singh, Chandani Sharma and B. K. Singh, “A review on Automatic Image Forgery Classification Using Advanced Deep Learning Techniques”, ICDIS, Advances in Data & Information Sciences, Springer Nature, 25 Nov, 2022.
Anshul. K. Singh, Chandani Sharma and B. K. Singh, “Image Forgery Localization and Detection using Multiple Deep Learning Algorithm with ELA” ICFIRTP 2022, IEEE, 2023.
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