Current Status, Challenges and Prospects of Face Fraud Detection

Face fraud detection is an important technology to ensure the security of face recognition systems and is widely used in identity authentication, financial payment, smart security and other fields. With the increasing sophistication of fraud attack methods, especially the development of high-quality deep fakes and 3D mask technology, face fraud detection faces severe challenges. This paper systematically reviews the current status of face fraud detection research, from traditional methods to advanced technologies based on deep learning, and analyzes mainstream datasets and performance evaluation indicators. At the same time, this paper summarizes the main challenges in this field, including insufficient data diversity, generalization ability of models, and real-time issues. In response to these challenges, current solutions and technical trends such as data augmentation, adversarial training, and lightweight model design are explored. Finally, this paper looks forward to future development directions, including cross-modal data fusion, more efficient detection algorithms, and the establishment of legal and ethical norms, to provide a comprehensive reference for relevant researchers and promote the development and application of face fraud detection technology.