## 4 Discussion
Experimental results show that YOLOv9-ARC achieves a 3.5% improvement in mAP over the baseline model on the DFDC dataset and outperforms YOLOv5 and EfficientNet in noisy and low-resolution conditions. Analysis indicates that AKConv adaptively adjusts the receptive field based on input scales, improving the model's ability to capture fine details, while CBAM enhances focus on key regions, reducing false detections. These advancements enable YOLOv9-ARC to maintain high detection accuracy even in complex environments, addressing gaps in existing research on low-quality fake face detection. Despite the strong performance of YOLOv9-ARC in detecting low-quality forged facial images, several research opportunities remain across different levels of difficulty and innovation. As shown in Figure 9, future work can be structured into three progressive layers. At the fundamental level, efforts should focus on constructing diverse and representative datasets. This includes collecting and synthesizing forged samples under extreme lighting, occlusion, and demographic variations, using both generative models and real-world data. Benchmarking across multiple datasets with standardized evaluation metrics (e.g., accuracy, recall) is also essential for understanding YOLOv9-ARC's strengths and limitations compared to other state-of-the-art detectors. In the advanced research stage, model robustness must be strengthened through adversarial training strategies and stress testing with examples generated by attacks like FGSM and PGD. Furthermore, adapting the model to detect forgeries created by emerging techniques such as diffusion models and advanced GANs requires structural enhancements in feature extraction and classification modules. Moving into high-level innovation, future directions include the development of cross-modal detection systems that fuse video, audio, and text information to enhance robustness. Additionally, the integration of explainable AI (XAI) tools such as saliency maps and Layer-wise Relevance Propagation (LRP) can provide transparency and accountability, while fairness audits are crucial to ensure equitable model performance across demographic groups. Future research should focus on optimizing computational efficiency to enable deployment in resource-constrained environments, such as mobile devices or embedded systems. Specifically, we plan to integrate lightweight neural network architectures (e.g., MobileNet, ShuffleNet) into the backbone of YOLOv9-ARC. These architectures are specifically designed to reduce the number of parameters and floating-point operations (FLOPs), which are key factors influencing model efficiency. For example, MobileNet uses depthwise separable convolutions, which significantly reduce the computational load by breaking the convolution operation into two parts: depthwise convolution (for each input channel) and pointwise convolution (to combine the outputs). In addition to using lightweight backbones, we also consider applying model pruning techniques to remove redundant connections and neurons that do not significantly contribute to the model's performance. This will help further reduce the model size and  
Figure 9: YOLOv9-ARC future research roadmap. This diagram outlines a three-stage research framework to improve low-quality deepfake face detection: data enhancement, adversarial defense, and cross-modal innovations. increase inference speed. Additionally, we plan to use knowledge distillation to transfer the knowledge from a larger, more complex model (teacher model) to a smaller, more efficient model (student model). By leveraging the student model's compactness, we aim to maintain detection accuracy while significantly reducing computational overhead. These strategies (lightweight architecture, pruning, and distillation) are specifically targeted at reducing inference time, memory footprint, and power consumption, all of which are critical when deploying models in resource-constrained environments such as mobile devices and embedded systems.