## 5 Conclusions  
In order to solve the problem of poor detection accuracy of deepfake face detection models in the real environment due to the inclusion of low-quality facial images, this paper presents a detection model YOLOv9-ARC. A low-quality face image usually contains loads of noise, artifacts, and other deleterious elements; thus, after we improve the feature extraction module (shown in Figure 2) in YOLOv9's baseline architecture, the model has better performance in feature extraction and feature fusion. Moreover, the CBAM attention mechanism is integrated with the backbone network of the original model, fostering the model's attention on necessary information and minimizing the occurrence of irrelevant or repetitive information. The YOLOv9-ARC detection models show better performance on low-quality images than existing alternatives, as evidenced by the results of a number of comparative and ablation studies on the DFDC dataset. Nevertheless, there remains room for enhancement regarding detection performance and model efficiency. In future work, we plan to optimize the speed of detection and lower the computational cost of the model.  
Conceptualization and design: [PERSON]; data collection and experimentation: [PERSON], Y.H. and F.X.; data analysis: [PERSON], Y.G. and W.S.; writing and revision: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shandong Provincial Science and Technology-Based Small and Medium-Sized Enterprises Innovation Capacity Improvement Project, grant number 2024 TSGC0158, and supported by Mr. [PERSON], an employee of Shandong Xinhua'an Information Technology Co., Ltd.  
This study used the publicly released DeepFake Detection Challenge (DFDC) dataset. Ethical review and approval were waived due to the fact that all video materials in the dataset were collected by Meta (formerly Facebook) from contracted actors under signed informed consent. Meta explicitly states that the dataset is intended solely for non-commercial academic research, and prior to its public release, all data were processed for identity anonymization and use restriction. Therefore, ethical clearance was obtained by Meta during data acquisition, and the dataset includes appropriate legal and ethical safeguards.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. The DFDC dataset consists of video materials collected from contracted actors who provided informed consent for participation. Written informed consent has been obtained from the participants to publish the associated data.  
**Data Availability Statement:** The data presented in this study are available in the DFDC dataset. These data were derived from the following resources available in the public domain: [[https://ai.meta.com/datasets/dfdc/](https://ai.meta.com/datasets/dfdc/)]([https://ai.meta.com/datasets/dfdc/](https://ai.meta.com/datasets/dfdc/)) (accessed on 22 June 2025).  
**Acknowledgments:** I would like to sincerely thank the students from Shandong University of Science and Technology for their diligence, intelligence, and relentless pursuit throughout the research process. Their contributions have been crucial to the successful completion of this study. Once again, I express my heartfelt gratitude to all the individuals and organizations who have supported and assisted this research.  
**Conflicts of Interest:** The authors declare that this study received funding from Shandong Xinhua'an Information Technology Co., Ltd. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.