FOCAL: A Forgery Localization Framework Based on Video Coding Self-Consistency

IEEE Open Journal of Signal Processing(2021)

引用 6|浏览24
暂无评分
摘要
Forgery operations on video contents are nowadays within the reach of anyone, thanks to the availability of powerful and user-friendly editing software. Integrity verification and authentication of videos represent a major interest in both journalism (e.g., fake news debunking) and legal environments dealing with digital evidence (e.g., courts of law). While several strategies and different forensics traces have been proposed in recent years, latest solutions aim at increasing the accuracy by combining multiple detectors and features. This paper presents a video forgery localization framework that verifies the self-consistency of coding traces between and within video frames by fusing the information derived from a set of independent feature descriptors. The feature extraction step is carried out by means of an explainable convolutional neural network architecture, specifically designed to look for and classify coding artifacts. The overall framework was validated in two typical forgery scenarios: temporal and spatial splicing. Experimental results show an improvement to the state of the art on temporal splicing localization as well as promising performance in the newly tackled case of spatial splicing, on both synthetic and real-world videos.
更多
查看译文
关键词
Forgery detection,multimedia forensics,video codecs,video forensics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要