A passive forensic method for video: Exposing dynamic object removal and frame duplication in the digital video using sensor noise features

Abstract

The purpose of this paper is to detect tampering in the video using passive forensic method. With the extensive availability of sophisticated video editing software and tools, it has become easy to forge the video using different tampering techniques. A well-known video tampering technique is to du plicate the frames or manipulate the contents of the frames to remove and hide the objects or person in a video. Video forensic is necessary because people tampered the video to get justice from court of law on the basis of video evidence, disgrace the important celebrity and to hide or expose some unwanted object. In this paper, we propose a passive forensic method to expose dynamic object removal and frames duplication by detecting the noise variance between original and tampered video frames. The proposed method exploits sensor noise features, extracted from each frame of the video using Discrete Wavelet Transform (DWT) and nonlinear thresholding such as Hard and Soft with Stein’s Unbiased Risk Estimator (SURE) shrinkage. Gaussian Mixture Density (GMD) is used as Bayesian classifier and Expectation-Maximization (EM) algorithm set the parameters of the GMD. Implementation results demonstrate that we are getting commendable processing speed, accuracy 97.36-96.26%, recall 99.80-98.94% and precision 97.34-87.95% respectively for object removal and frame duplication detection in comparison to existing methods. Proposed method successfully detect and localize forgery in the given video.