## 2 Materials and Methods  
### Model Overview  
In the field of object detection, YOLOv9 [23] emerged as a new breakthrough in 2024, setting a new benchmark for efficiency and accuracy. However, when dealing with low-quality facial images, the YOLOv9 model begins to show inefficiencies, leading to a decline in detection performance. To address these issues, this paper proposes the YOLOv9-ARC algorithm for counterfeit detection in low-quality facial images. The full network architecture is depicted in Figure 1. This framework is composed of three primary components. The Backbone is responsible for extracting initial features from the input image. The Convolutional Block Attention Module (CBAM) [24] is integrated into the Backbone to focus on important regions of the image. By leveraging both spatial and channel attention mechanisms, CBAM enhances critical information while suppressing noise and irrelevant regions, thereby improving the feature extraction capability for low-quality images. The Adaptive Kernel Convolution (AKConv) [25] is combined with the RepNCSPELAN4 module to expand the receptive field and enable the network to capture finer spatial features, enhancing its adaptability to images of varying quality and size. Additionally, the SPPFELAN module further aggregates and refines these features. The Neck connects the Backbone and the Head, primarily processing and fusing the features extracted by the Backbone to enhance multi-scale feature representation. This is particularly beneficial for detecting objects of varying sizes in low-quality images. The Neck also employs AKConv to dynamically adjust the convolution kernel size, enabling it to handle feature maps at different scales. Through CBFuse operations, multiple feature maps are fused, integrating information from different levels into a unified feature representation. Furthermore, UpSample and Concat operations upsample and concatenate feature maps of different resolutions, generating higher-dimensional feature maps and ensuring effective multi-scale information fusion. Finally, the Head utilizes the refined features from the Neck to generate the final detection results. This component includes multiple Conv layers to further refine the features, ensuring precise detection outputs. Multiple Conv2d layers are used to generate target location and category information, while the Bbox Loss is computed to optimize the model through bounding box regression. By integrating the CBAM attention mechanism, AKConv adaptive convolution, and multi-scale feature fusion techniques, YOLOv9-ARC significantly enhances object detection capabilities in low-quality images. It effectively addresses challenges such as noise and blur, providing accurate forgery detection results. As illustrated in Figures 1 and 2, the proposed YOLOv9-ARC model demonstrates significant advantages over the original YOLOv9 model when processing low-quality facial images, owing to the integration of the CBAM attention mechanism and AKConv adaptive convolution. The CBAM mechanism enables the model to focus on critical regions of the image, particularly edge and texture features that are essential for low-quality images. Meanwhile, AKConv dynamically adjusts the convolution kernel size to adapt to multi-scale feature extraction, effectively capturing fine-grained details in the images. In terms of feature fusion, the proposed model employs the CBFuse operation to better integrate features from different levels, enhancing the ability to combine multi-scale information. This is particularly crucial for low-quality images, where facial features may be fragmented or blurred. In contrast, although the original YOLOv9 model also utilizes Conv layers and concatenation operations, its lack of the CBAM attention mechanism limits its ability to prioritize key features in low-quality images, resulting in inferior performance compared  
Figure 1: YOLOv9-ARC architecture diagram.