Recent advances in content generation technologies (widely known as DeepFakes) along with the online proliferation of manipulated media content render the detection of such manipulations a task of increasing importance. Even though there are many DeepFake detection methods, only a few focus on the impact of dataset preprocessing and the aggregation of frame-level to video-level prediction on model performance. In this paper, we propose a pre-processing step to improve the training data quality and examine its effect on the performance of DeepFake detection. We also propose and evaluate the effect of video-level prediction aggregation approaches. Experimental results show that the proposed pre-processing approach leads to considerable improvements in the performance of detection models, and the proposed prediction aggregation scheme further boosts the detection efficiency in cases where there are multiple faces in a video.
HOLA: Enhancing Audio-visual Deepfake Detection via Hierarchical Contextual Aggregations and Efficient Pre-training
Xi'an Jiaotong University（西安交通大学）；Zhengzhou University（郑州大学）；University of Science and Technology of China（中国科学技术大学）；Dalian University of Technology（大连理工大学）；Zhejiang Lab（浙江实验室）
Investigating the Viability of Employing Multi-modal Large Language Models in the Context of Audio Deepfake Detection
Indian Institute of Technology, Ropar, India（印度理工学院罗帕尔分校）；Machine Intelligence Group, Birla Institute of Technology and Science, Pilani, Hyderabad Campus, India（比拉理工科学院帕利尼 Hyderabad 分校机器智能小组）；Monash University, Melbourne, Australia（墨尔本大学）
Towards Robust DeepFake Detection under Unstable Face Sequences: Adaptive Sparse Graph Embedding with Order-Free Representation and Explicit Laplacian Spectral Prior
Institute of Intelligent Systems, College of Artificial Intelligence and Green Energy, National Yang Ming Chiao Tung University（智能系统研究所，人工智能与绿色能源学院，国立阳明交通大学）；Institute of Data Science, National Cheng Kung University（数据科学研究所，国立成功大学）
Towards Open-world Generalized Deepfake Detection: General Feature Extraction via Unsupervised Domain Adaptation
School of Computer Science and Engineering（计算机科学与工程学院）；Sun Yat-sen University（中山大学）；State Key Laboratory of Mathematical Engineering and Advanced Computing（数学工程与先进计算国家重点实验室）
