The field of deepfake detection and privacy protection is rapidly evolving, with a focus on developing innovative methods to identify and mitigate the risks associated with fake multimedia content. Recent research has explored the use of visual detail enhanced self-correction frameworks, multi-modal face anti-spoofing techniques, and ensemble-based deepfake detection approaches to improve the accuracy and robustness of detection systems. Additionally, there is a growing interest in addressing the challenges of privacy policy complexity and inconsistencies, particularly in the context of online banking and data sharing practices. Noteworthy papers in this area include CorrDetail, which introduces a visual detail enhanced self-correction framework for face forgery detection, and Layered, Overlapping, and Inconsistent, which presents a large-scale analysis of the multiple privacy policies and controls of U.S. banks. Multi-Modal Face Anti-Spoofing via Cross-Modal Feature Transitions also presents a novel approach to multi-modal face anti-spoofing, while DATABench provides a comprehensive evaluation of dataset auditing methods from an adversarial perspective. Ensemble-Based Deepfake Detection using State-of-the-Art Models with Robust Cross-Dataset Generalisation demonstrates the effectiveness of ensemble-based approaches in improving the generalization of deepfake detection systems. Furthermore, research in speech emotion recognition, such as A Novel Hybrid Deep Learning Technique for Speech Emotion Detection using Feature Engineering, has achieved high accuracy in recognizing emotions from speech. End-to-end Acoustic-linguistic Emotion and Intent Recognition Enhanced by Semi-supervised Learning showcases the potential of semi-supervised learning in improving model performance in speech emotion and intent recognition. Overall, these advances have significant implications for the development of more effective and reliable deepfake detection and privacy protection systems.