Abstract: A facial expression-based detection method for deepfake by generative artificial intelligence (AI) constructs an AIR-Face facial dataset for generative AI-created face detection training, and uses an untrained information feature space for real and fake classification. Nearest linear detection is performed in this space to significantly improve the generalization ability of detecting fake images, especially those created by new methods such as diffusion models or autoregressive models. The detection method improves the performance of extracting features of generative AI-created faces through phased trainings, and detects generative AI-created faces through the feature space. Compared with other methods, the detection method scientifically and effectively improves the accuracy of generative AI-created face recognition, and fully mines the potential semantic information of generative AI-created faces through phased trainings.

Abstract: A personalized comment recommendation method based on a link prediction model of a graph bidirectional aggregation network. In a user-comment bipartite graph, comment features are aggregated into a user feature. A social network is used to fuse a neighbor feature of a user to obtain an embedding representation of the user. The embedding representation of the user is aggregated into a comment after an original feature of the user is removed, and the embedding representation of the user is adjusted based on a difference before and after comment aggregation. On this basis, a forwarding network is used to calculate a score of an edge based on an inner product of user node features at both ends of the edge, and finally make a recommendation based on the score. Furthermore, a recommendation system converts a comment recommendation task into a link prediction task between users in a small range.

Abstract: A personalized comment recommendation method based on a link prediction model of a graph bidirectional aggregation network. In a user-comment bipartite graph, comment features are aggregated into a user feature. A social network is used to fuse a neighbor feature of a user to obtain an embedding representation of the user. The embedding representation of the user is aggregated into a comment after an original feature of the user is removed, and the embedding representation of the user is adjusted based on a difference before and after comment aggregation. On this basis, a forwarding network is used to calculate a score of an edge based on an inner product of user node features at both ends of the edge, and finally make a recommendation based on the score. Furthermore, a recommendation system converts a comment recommendation task into a link prediction task between users in a small range.