This paper proposes a method for detecting forged objects in videos that include dynamic scenes such as dynamic background or non-stationary scenes. In order to adapt to dynamic scenes, we combine Convolutional Neural Network and Recurrent Neural Network. This enables us to consider spatio-temporal consistency of videos. We also construct new video forgery databases for object modification as well as object removal. Our proposed method using Convolutional Long Short-Term Memory achieved Area-Under-Curve (AUC) 0.977 and Equal-Error-Rate (EER) 0.061 on the object addition database. We also achieved AUC 0.872 and EER 0.219 on the object modification database.
CITATION STYLE
Kono, K., Yoshida, T., Ohshiro, S., & Babaguchi, N. (2020). Passive Video Forgery Detection Considering Spatio-Temporal Consistency. In Advances in Intelligent Systems and Computing (Vol. 942, pp. 381–391). Springer Verlag. https://doi.org/10.1007/978-3-030-17065-3_38
Mendeley helps you to discover research relevant for your work.