TransCMFD: An adaptive transformer for copy-move forgery detection

Abstract

Copy-move forgery is one of the most common image tampering methods. It is a frequently employed method for manipulating evidence or deceiving the public by hiding some objects in an image or replicating significant objects. Therefore, it is crucial to focus on copy-move forgery detection. In this paper, we propose TransCMFD as a new transformer-based model for detecting copy-move forgery. We propose an adaptive transformer encoder and combine the traditional convolution encoder–decoder to capture different global and local features of the forgery image, respectively. This can enhance the model’s comprehension of the impact of forgery across different tampered image regions. To allow the model to concentrate more on the tampered regions that resemble the original regions, we introduce a similarity detection module. Moreover, to enhance the localization accuracy of the tampered regions, we design an adaptive loss function combination strategy that incorporates the DICE coefficient loss and binary cross-entropy loss. We perform comprehensive experiments on both synthetic and four publicly available datasets. The results show that our model has better performance in copy-move forgery detection compared to baseline methods, and it has remarkable robustness to some common image attacks such as noise addition attacks, image blurring attacks, and color reduction attacks.