The development of Generative Adversarial Networks (GANs) has revolutionized image generation and editing. However, the capacity to create realistic images presents serious security concerns, particularly in the context of face-based payment systems. Deepfakes leverages GANs to generate manipulated videos or images, which may present opportunities for identity theft and fraudulent transactions. For instance, perpetrators employ Deepfakes technology to forge identifying information about victims, such as transplanting their faces into fake videos or images to make it appear like they are performing activities they have never done before. To address this growing concern, this study proposes a deep learning-based detection method utilizing an improved convolutional neural network (CNN) model. The proposed model comprises two key modules, namely the Multi-scale Attention (MA) module and the Halo Attention (HA) module. Specifically, MA is designed to recognize faces and other details in the forged image. HA is built to focus on localized regions of the image. Experimental results show that the proposed model scores 97.12 and 99.32 on FF++ (HQ) dataset and 91.26 and 95.43 on FF++ (LQ) dataset in terms of ACC and AUC, respectively. The remarkable accuracy and performance make it a dependable solution for safeguarding face payment systems.