Research on Privacy-Preserving Action Recognition Method Based on Adversarial Learning and Feature Enhancement

Video action recognition technology provides technical support for automated monitoring and early event warning, liberating human resources, preventing anomalies in advance, and handling events more promptly. However, the issue of user privacy leakage is also a growing concern. This paper discusses how to conduct action recognition while protecting privacy, proposing a generative adversarial network architecture that combines a multi-scale feature fusion generator with a spatiotemporal consistency discriminator. The network continuously enhances its capabilities through adversarial training strategies to protect facial privacy in videos. This network extracts features at different levels in the video through a multi-scale feature fusion mechanism so that the video after facial privacy protection still maintains a high degree of realism at the same time, to ensure that the accuracy of action recognition is not compromised, the feature enhancement module is designed to enhance action features and inhibit the privacy features significantly C3D is used as the action recognition model to accurately recognize a variety of actions in the video, such as running, jumping, falling, realizing the action analysis of video content. In this paper, the proposed method is evaluated in terms of privacy protection level and action recognition performance. Experiments are conducted on three datasets, LFW, HMDB51, and Hollywood2, and the results show that this framework effectively protects personal information while maintaining high action recognition accuracy.