The rise of deepfakes has significantly heightened concerns for privacy and the authenticity of digital media, bringing widespread attention to face forgery detection. Previous deepfake detection methods mostly depend on low-level textural features vulnerable to perturbations and fall short of detecting unseen forgery methods. In contrast, high-level semantic features are less susceptible to perturbations and not limited to forgery-specific artifacts, thus having stronger generalization. Motivated by this, we propose a detection method that utilizes high-level semantic features of faces to identify inconsistencies in temporal domain. We introduce UniForensics, a novel deepfake detection framework that leverages a transformer-based video classification network, initialized with a meta-functional face encoder for enriched facial representation. In this way, we can take advantage of both the powerful spatio-temporal model and the high-level semantic information of faces. Furthermore, to leverage easily accessible real face data and guide the model in focusing on spatio-temporal features, we design a Dynamic Video Self-Blending (DVSB) method to efficiently generate training samples with diverse spatio-temporal forgery traces using real facial videos. Based on this, we advance our framework with a two-stage training approach: The first stage employs a novel self-supervised contrastive learning, where we encourage the network to focus on forgery traces by impelling videos generated by the same forgery process to have similar representations. On the basis of the representation learned in the first stage, the second stage involves fine-tuning on face forgery detection dataset to build a deepfake detector. Extensive experiments validates that UniForensics outperforms existing face forgery detection methods in generalization ability and robustness. In particular, our method achieves 95.3% and 77.2% cross dataset AUC on the challenging Celeb-DFv2 and DFDC respectively. Code will be made publicly available.

This study aims to evaluate Chinese short-answer questions according to the Structure of Observed Learning Outcomes (SOLO) taxonomy, which divides students' responses into five levels, from prestructural to extended abstract. To automate the evaluation process, supervised classification was used. For this purpose, three deep-learning models were adopted: LSTM, C-LSTM, Nezha-BiLSTM. Results showed that the accuracy of LSTM was the lowest at 0.83, the accuracy of C-LSTM was 0.89, and the accuracy of Nezha-BiLSTM was the highest at 0.90. And the accuracy of each classification model is high in the prestructural and multistructural responses. The method proposed could help teachers perform scoring more efficiently.