## 4 Discussion  
This study proposes a novel method for detecting low-quality fake facial images based on the YOLOv9-ARC model and validates its effectiveness through extensive experiments. The experimental results demonstrate that YOLOv9-ARC achieves a mean average precision (mAP) of 75.1% on the DFDC (DeepFake Detection Challenge) dataset, representing a 3.5% improvement over the baseline model, highlighting the model's superior detection performance in complex environments. The primary advantage of YOLOv9-ARC lies in the integration of the Adaptive Kernel Convolution (AKConv) module, which dynamically adjusts the receptive field to enable more effective multi-scale feature extraction. Additionally, the Convolutional Block Attention Module (CBAM) enhances the model's ability to focus on critical regions, further improving detection accuracy. Experimental results confirm that CBAM significantly boosts both precision and recall, validating its effectiveness in optimizing feature representation. A deeper analysis reveals that the combination of AKConv and CBAM plays a critical role in the model's success on low-quality images. AKConv allows adaptive kernel size adjustment based on local features, which helps mitigate challenges caused by noise, blur, and reduced resolution. Meanwhile, CBAM refines feature representation by emphasizing discriminative regions and suppressing redundant or noisy features. This synergy is particularly beneficial for detecting visually degraded or compressed deepfakes, which are prevalent in the DFDC dataset. However, the DFDC dataset still contains challenging conditions such as extreme lighting, occlusion, and large pose variations, which may affect detection outcomes. Although YOLOv9-ARC improves most performance metrics, it can sometimes generate false positives due to its high sensitivity to subtle artifacts, resulting in slightly lower precision in specific cases. The current evaluation is limited to a single dataset, and the model's generalizability requires further validation on diverse datasets such as FaceForensics++ and Celeb-DF. Potential data bias and lack of adversarial robustness testing also present limitations. Future work will address these issues through cross-dataset evaluation, deployment optimization, and the integration of explainable AI techniques to improve interpretability and transparency. Compared to prior research, YOLOv9-ARC demonstrates superior performance in detecting low-quality fake facial images. Traditional methods such as Faster R-CNN [26] and EfficientNet [27] excel on high-quality facial image datasets but exhibit significant performance degradation under conditions of low resolution, noise, or blur. Faster R-CNN, with its region proposal-based detection mechanism, offers strong accuracy but suffers from high computational complexity, making it unsuitable for real-time applications. EfficientNet, while effective for standard image classification tasks, struggles with low-resolution and high-noise environments due to its limited feature extraction capabilities. Recent advancements in YOLO series models [28; 29; 30] have made them popular for face detection tasks due to their efficiency and robust feature extraction. However, studiesreveal that YOLOv3 and YOLOv5 still face challenges such as false positives and missed detections in low-quality fake face detection. This is primarily due to the limitations of standard convolution in capturing multi-scale information and the lack of mechanisms to focus on key regions. While YOLOv3 employs a fixed-size receptive field, limiting its effectiveness for small-object detection, YOLOv5 improves computational efficiency through depth-wise separable convolution but remains constrained in feature extraction for high-noise data. In contrast, YOLOv9-ARC introduces the AKConv variable convolution kernel and CBAM attention mechanism, significantly enhancing the model's detection capability for low-quality images.