Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting Doctored Images Using Camera Response Normality and Consistency
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Exposing digital forgeries by detecting inconsistencies in lighting
MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
Noise Estimation from a Single Image
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Exposing digital forgeries through chromatic aberration
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Exposing digital forgeries in video by detecting duplication
Proceedings of the 9th workshop on Multimedia & security
Detecting Video Forgeries Based on Noise Characteristics
PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Exposing Digital Forgeries in Interlaced and Deinterlaced Video
IEEE Transactions on Information Forensics and Security - Part 1
Novel blind video forgery detection using markov models on motion residue
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Detecting removed object from video with stationary background
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
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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.