Multibranch Collaboration and Segmented Training Network for Image Forgery Comprehensive Detection

With the rise of sophisticated image manipulation techniques, image detection forgeries have become increasingly challenging. This paper presents a novel deep learning framework, the multibranch collaboration and segmented training network (MBC‐STN), for robust image forgery detection. MBC‐STN addresses the challenges posed by diverse forgery types and complex noisy environments through a multibranch collaborative architecture that integrates three specialized branches: the forgery edge‐aware branch (FEB), the noise‐aware branch (NB), and the color‐aware branch (CB). These branches capture image forgery features from multiple dimensions, significantly enhancing the precision and robustness of forgery detection. Additionally, MBC‐STN employs a segmented training strategy to optimize the performance of different branches in stages, improving training efficiency, and model adaptability. Three loss functions (balanced binary cross‐entropy loss, balanced dice loss, and binary cross‐entropy loss) are employed to guide MBC‐STN in learning manipulated traces and identifying forgery regions. Extensive experiments on multiple datasets demonstrate that MBC‐STN outperforms state‐of‐the‐art methods maintain robustness under different postprocessing conditions.