Research Article
SHIELD: A Specialized Dataset for Hybrid Blind Forensics of World Leaders
@INPROCEEDINGS{10.1007/978-3-031-56583-0_6, author={Qingran Lin and Xiang Li and Beilin Chu and Renying Wang and Xianhao Chen and Yuzhe Mao and Zhen Yang and Linna Zhou and Weike You}, title={SHIELD: A Specialized Dataset for Hybrid Blind Forensics of World Leaders}, proceedings={Digital Forensics and Cyber Crime. 14th EAI International Conference, ICDF2C 2023, New York City, NY, USA, November 30, 2023, Proceedings, Part II}, proceedings_a={ICDF2C PART 2}, year={2024}, month={4}, keywords={Video forensics Deepfake detection Dataset}, doi={10.1007/978-3-031-56583-0_6} }- Qingran Lin
Xiang Li
Beilin Chu
Renying Wang
Xianhao Chen
Yuzhe Mao
Zhen Yang
Linna Zhou
Weike You
Year: 2024
SHIELD: A Specialized Dataset for Hybrid Blind Forensics of World Leaders
ICDF2C PART 2
Springer
DOI: 10.1007/978-3-031-56583-0_6
Abstract
The speech videos of public figures, such as movie celebrities and world leaders, have an extensive influence on the Internet. However, the authenticity of these videos is often difficult to ascertain. These videos may have been carefully imitated by comedians or manipulated using Deepfake methods, which creates significant obstacles for the video forensics of specific characters. Moreover, the vast amount of data on social networking platforms renders manual screening impractical. To specifically address this issue, we present SHIELD, which stands forSpecialized dataset forHybrid blInd forEnsics of worLd leaDers. Unlike most previous public Deepfake datasets that only contain Deepfake samples, this dataset exquisitely includes a collection that can quickly test this issue, encompassing both impersonator and Deepfake videos. We provide a detailed dataset production process and conduct an elaborate experiment under the hybrid blind detection scenario. Our findings reveal the limitations of existing methods, demonstrate the potential of identity-based models, and illustrate the increased challenges posed by SHIELD.
