Facial recognition systems have become increasingly significant in public security efforts. They play a crucial role in apprehending criminals and locating missing children and elderly individuals. Nevertheless, in practical applications, around 30% to 50% of images are obstructed to varied extents, for as by the presence of masks, glasses, or hats. Repairing the masked facial images will enhance face recognition accuracy by 10% to 20%. Simultaneously, market research indicates a rising demand for efficient facial recognition technology within the security and surveillance sectors, with projections suggesting that the global facial recognition market would exceed US＄3 billion by 2025. Therefore, finding a prompt and efficient solution to fix the masked face and enhance its accuracy has become a pressing issue that has to be resolved. Currently, the generative adversarial network has shown excellent performance in the field of image restoration, with high precision and good quality of restoration results, but it consumes a lot of computing resources. Based on this, this paper proposes a model architecture that uses the U-Net network to replace the generator in the generative adversarial network, and replaces all traditional convolutional layers with Depthwise Separable Convolutional (DWSC) to make the entire network lightweight. Ultimately, We utilise the Peak Signal-to-Noise Ratio (PSNR) value to assess the efficacy of the developed model. We select samples with occlusion levels ranging from 10%–15% and 20%–30%, yielding PSNR values of 35.51 and 30.33, respectively. In contrast, the PSNR values of the three predominant algorithms in image restoration—PM, ShiftNet, and PICNet—are all below 30, demonstrating the superiority of the model presented in this paper. However, the model presented in this work possesses certain drawbacks. This work employs solely black rectangles to replicate real-life occlusions. Future study should utilise tangible objects, like as sunglasses and masks, to directly imitate occlusions, so enhancing the accuracy of the restoration effect. The model presented in this study can be further expanded from image restoration to video restoration to investigate the potential for dynamic occlusion repair.

Rapid development of deepfake technology led to the spread of forged audios and videos across network platforms, presenting risks for numerous countries, societies, and individuals, and posing a serious threat to cyberspace security. To address the problem of insufficient extraction of spatial features and the fact that temporal features are not considered in the deepfake video detection, we propose a detection method based on improved CapsNet and temporal–spatial features (iCapsNet–TSF). First, the dynamic routing algorithm of CapsNet is improved using weight initialization and updating. Then, the optical flow algorithm is used to extract interframe temporal features of the videos to form a dataset of temporal–spatial features. Finally, the iCapsNet model is employed to fully learn the temporal–spatial features of facial videos, and the results are fused. Experimental results show that the detection accuracy of iCapsNet–TSF reaches 94.07%, 98.83%, and 98.50% on the Celeb-DF, FaceSwap, and Deepfakes datasets, respectively, displaying a better performance than most existing mainstream algorithms. The iCapsNet–TSF method combines the capsule network and the optical flow algorithm, providing a novel strategy for the deepfake detection, which is of great significance to the prevention of deepfake attacks and the preservation of cyberspace security.