Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features. (November 2022)

• Record Type: Journal Article
• Authors: Yoon, Minseok; Nam, Seung-Hun; Yu, In-Jae; Ahn, Wonhyuk; Kwon, Myung-Joon; Lee, Heung-Kyu
• Is Part Of: Forensic science international. Volume 340(2022)
• Publication Date: 2022-11
• Subjects: Video forensics -- Frame-rate conversion detection -- Frame interpolation scheme -- Convolutional neural network -- Residual features -- Spatiotemporal features; Medical jurisprudence -- Periodicals; Chemistry, Forensic -- Periodicals; Forensic Medicine -- Periodicals; Médecine légale -- Périodiques; Chimie légale -- Périodiques; Gerechtelijke geneeskunde; Gerechtelijke chemie; Gerechtelijke psychiatrie; Chemistry, Forensic; Medical jurisprudence; Electronic journals; Periodicals; Electronic journals; 614.1
• DOI: 10.1016/j.forsciint.2022.111442
• Languages: English
• ISSNs: 0379-0738

Abstract

Abstract: With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continuity of videos with a lower frame-rate and is used by malicious counterfeiters in video tampering such as generating fake frame-rate video without improving the quality or mixing temporally spliced videos. FRUC is based on frame interpolation schemes and subtle artifacts that remain in interpolated frames are often difficult to distinguish. Hence, detecting such forgery traces is a critical issue in video forensics. This paper proposes a frame-rate conversion detection network (FCDNet) that learns forensic features caused by FRUC in an end-to-end fashion. The proposed network uses a stack of consecutive frames as the input and effectively learns interpolation artifacts using network blocks to learn spatiotemporal features. Moreover, it can cover the following three types of frame interpolation schemes: nearest neighbor interpolation, bilinear interpolation, and motion-compensated interpolation. In contrast to existing methods that exploit all frames to verify integrity, the proposed approach achieves a high detection speed because it observes only six frames to test its authenticity. Extensive experiments were conducted with conventional forensic methods and neural networks for video forensics to validate our research. The proposed work achieved an outstanding performance in terms of detecting the interpolated artifacts of FRUC. The experimental results also demonstrate that our model is robust against an unseen dataset, unlearned frame-rate, and unlearned quality factor. Furthermore, FCDNet can precisely localize the tampered region applied to manipulation along the time-domain through temporal localization.