## 1 Introduction  
In recent years, due to rapid advancements in deep generative models [1; 2; 3], a multitude of image editing applications [4; 5] have gained widespread popularity among the general public. However, there is growing concern regarding the potential misuse of these editing techniques for image forgery. The act of image forgery carries significant implications across various domains, including instances of academic misconduct and the dissemination of counterfeit images. Such occurrences emphasize the urgent need to intensify our focus on detecting image forgery. Consequently, the development of effective methodologies for accurately identifying tampered regions in images has become of utmost importance. Generally, image manipulation techniques are categorized into three groups. For each type of manipulation, researchers have proposed numerous approaches for localizing image manipulations, such as splicing [6; 7; 8; 9; 10], copy-and-paste [11; 12; 13], and removal [14; 15]. As depicted in Figure 1, these methods often operate at the object level to create semantically coherent and persuasive images, involving the addition or removal of objects within the image. Drawing inspiration from this, we contend that image manipulation detection and localization should consider consistency of objects with one another, necessitating the establishment of long-range relations. While certain CNN-based methods [16; 17; 18; 19; 20] have achieved some success in this regard, building long-range relations using these methods requires continuous downsampling and convolution operations. This approach presents several drawbacks: (1) excessively deep networks can encounter the problem of gradient disappearance, and (2) the gradual reduction in spatial resolution results in the disappearance of crucial local information essential for image manipulation detection and localization tasks. The Transformer architecture [21], originally employed in the realm of Natural Language Processing (NLP), has garnered significant attention within the computer vision community. One notable application is the SETR model, which replaces the encoder layers of traditional encoder-decoder networks with transformers. This modification has led to impressive outcomes in image segmentation tasks, establishing SETR as a state-of-the-art solution. While the Transformer excels at establishing long-range relationships, it does exhibit limitations in capturing intricate details. In the context of image forgery detection, the ability to capture fine details is crucial. Consequently, a pure Transformer-based approach akin to SETR does not yield satisfactory results in this domain. In recent research efforts, there have been attempts to combine CNNs and Transformers to leverage their respective strengths [22; 23]. For example, ObjectFormer [22] utilizes an RGB stream and a frequency stream, employing CNNs to extract features that are then processed by Transformers to establish long-range relationships and generate prediction masks. However, this approach mainly focuses on replacing convolutional layers with Transformers without effectively leveraging the advantages of both architectures. To fully harness the strength of CNNs combined with Transformers in manipulation detection, we propose a novel two-stream network called GP-Net. In GP-Net, the CNN-based branch and the Transformer-based branch operate in parallel, allowing each branch specialization in its respective strengths. To combine feature maps from both branches in an effective way, the TcFusion module is introduced by us, which efficiently fuses the feature maps to make predictions. Through an extensive ablation study, we optimize our network structure, resulting in improved performance compared to SOAT approaches across various datasets. In addition, we summarize the main contributions of our work:  
* GPNet represents a two-branch network which integrates CNNs and Transformers for image manipulation detection.