Copy-move forgery detection is a crucial research area within digital image forensics, as it focuses on identifying instances where objects in an image are duplicated and placed in different locations. The detection of such forgeries is particularly important in contexts where they can be exploited for malicious purposes. Recent years have witnessed an increased interest in distinguishing between the original and duplicated objects in copy-move forgeries, accompanied by the development of larger-scale datasets to facilitate this task. However, existing approaches to copy-move forgery detection and source/target differentiation often involve two separate steps or the design of individual end-to-end networks for each task. In this paper, we propose an innovative method that employs the transformer architecture in an end-to-end deep neural network. Our method aims to detect instances of copy-move forgery while simultaneously localizing the source and target regions. By utilizing this approach, we address the challenges posed by multi-object copy-move scenarios and report if there is a balance between the detection and differentiation tasks. To evaluate the performance of our proposed network, we conducted experiments on two publicly available copy-move datasets. The results and analysis aims to show the potential significance of our focus in balancing detection and distinguishment result and transferring the trained model in different datasets in the field.

翻译：复制-移动伪造检测是数字图像取证领域中的一项关键研究，其核心是识别图像中物体被复制并移至不同位置的情况。此类伪造检测在可能被用于恶意目的的背景下尤为重要。近年来，区分复制-移动伪造中的原始物体与复制物体的研究兴趣日益增长，同时更大规模的数据集也被开发以支持这一任务。然而，现有的复制-移动伪造检测与源/目标区分方法通常涉及两个独立步骤，或为每个任务设计单独的端到端网络。本文提出了一种创新方法，在端到端深度神经网络中采用Transformer架构。我们的方法旨在检测复制-移动伪造实例，同时定位源区域与目标区域。通过这一方法，我们应对多物体复制-移动场景带来的挑战，并报告检测与区分任务之间是否存在平衡。为评估所提出网络的性能，我们在两个公开的复制-移动数据集上进行了实验。结果与分析旨在展示我们关注检测与区分结果平衡的重要性，以及将训练模型迁移至不同数据集所具备的潜在意义。