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IMTNet: Improved multi-Task Copy-Move Forgery Detection Network with feature Decoupling and multi-feature Pyramid

Computers, Materials and Continua 2024年第3期80卷 4603-4620页

作者： Wang H. Jiang Z. Qian Q. Long Y. School of Information Guizhou University of Finance and Economics Guiyang 550025 China College of Big Data Statistics Guizhou University of Finance and Economics Guiyang 550025 China

Copy-Move Forgery Detection (CMFD) is a technique that is designed to identify image tampering and locate suspicious areas. However, the practicality of the CMFD is impeded by the scarcity of datasets, inadequate quality and quantity, and a narrow range of applicable tasks. These limitations significantly restrict the capacity and applicability of CMFD. To overcome the limitations of existing methods, a novel solution called IMTNet is proposed for CMFD by employing a feature decoupling approach. Firstly, this study formulates the objective task and network relationship as an optimization problem using transfer learning. Furthermore, it thoroughly discusses and analyzes the relationship between CMFD and deep network architecture by employing ResNet-50 during the optimization solving phase. Secondly, a quantitative comparison between fine-tuning and feature decoupling is conducted to evaluate the degree of similarity between the image classification and CMFD domains by the enhanced ResNet-50. Finally, suspicious regions are localized using a feature pyramid network with bottom-up path augmentation. Experimental results demonstrate that IMTNet achieves faster convergence, shorter training times, and favorable generalization performance compared to existing methods. Moreover, it is shown that IMTNet significantly outperforms fine-tuning based approaches in terms of accuracy and F1 Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： feature decoupling Image copy-move detection multi-scale feature pyramids passive forensics

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