TY  - EJOU
AU  - Wang, Huan 
AU  - Wang, Hong 
AU  - Jiang, Zhongyuan 
AU  - Qian, Qing 
AU  - Long, Yong 

TI  - IMTNet: Improved Multi-Task Copy-Move Forgery Detection Network with Feature Decoupling and Multi-Feature Pyramid
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 80
IS  - 3
SN  - 1546-2226

AB  - 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 <i>F</i><sub>1</sub>.
KW  - Image copy-move detection; feature decoupling; multi-scale feature pyramids; passive forensics

DO  - 10.32604/cmc.2024.053740