Detecting Forgery From Static-Scene Video Based on Inconsistency in Noise Level Functions

Recently developed video editing techniques have enabled us to create realistic synthesized videos. Therefore, using video data as evidence in places such as courts of law requires a method to detect forged videos. In this study, we developed an approach to detect suspicious regions in a video of a static scene on the basis of the noise characteristics. The image signal contains irradiance-dependent noise the variance of which is described by a noise level function (NLF) as a function of irradiance. We introduce a probabilistic model providing the inference of an NLF that controls the characteristics of the noise at each pixel. Forged pixels in the regions clipped from another video camera can be differentiated by using maximum a posteriori estimation for the noise model when the NLFs of the regions are inconsistent with the rest of the video. We demonstrate the effectiveness of our proposed method by adapting it to videos recorded indoors and outdoors. The proposed method enables us to highly accurately evaluate the per-pixel authenticity of the given video, which achieves denser estimation than prior work based on block-level validation. In addition, the proposed method can be applied to various kinds of videos such as those contaminated by large noise and recorded with any scan formats, which limits the applicability of the existing methods.