In this paper we present a new method for the detection of forgeries in digital videos, using the sensor's pattern noise. The camera pattern noise is a unique stochastic high frequency characteristic of imaging sensors and the detection of a forged frame in a video is determined by comparing the correlation between the noise within the frame itself and the reference pattern noise with an empirical threshold. The reference pattern is created for the identification of the camera and the authentication of the video too. Such a pattern is defined as self building because it is created from the video sequence during the time develop, with a technique applied frame per frame, by averaging the noise extracted from each frame. The method has been inherited from an existing system created by Fridrich et al.1 for still images. By using this method we are able to identify if all the scenes of a video sequence have been taken with the same camera and if the number and/or the content of the frames of the video have been modified. A large section of the paper is dedicated to the experimental results, where we demonstrate that it is possible to perform a reliable identification even from video that has undergone MPEG compression or frame interpolation.
Detection of malevolent changes in digital video for forensic applications
CALDELLI, ROBERTO;
2007-01-01
Abstract
In this paper we present a new method for the detection of forgeries in digital videos, using the sensor's pattern noise. The camera pattern noise is a unique stochastic high frequency characteristic of imaging sensors and the detection of a forged frame in a video is determined by comparing the correlation between the noise within the frame itself and the reference pattern noise with an empirical threshold. The reference pattern is created for the identification of the camera and the authentication of the video too. Such a pattern is defined as self building because it is created from the video sequence during the time develop, with a technique applied frame per frame, by averaging the noise extracted from each frame. The method has been inherited from an existing system created by Fridrich et al.1 for still images. By using this method we are able to identify if all the scenes of a video sequence have been taken with the same camera and if the number and/or the content of the frames of the video have been modified. A large section of the paper is dedicated to the experimental results, where we demonstrate that it is possible to perform a reliable identification even from video that has undergone MPEG compression or frame interpolation.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
