A Video Forgery Detection Using Discrete Wavelet Transform and Scale Invarient Feature Transform Techniques

Forensics suggests that the utilization of science and technology within the investigation and institution of facts. Therefore the video or completely different pictures will be transmission to and reconverted into another video by another laptop. Processed crime scene investigation (in some cases referred to as advanced legal science) could be a branch of measurable science as well as the recovery and examination of structure found in processed gadgets. Digital video forensics is a brand new research field which aims at validating the authenticity of videos by recovering information about their history. The fundamental problems which research found in the literature can be categorized into the natural, forgery detection, flow mapping, and source identification. Therefore, the originality and authenticity of videos or data in many cases become challenging problem. We propose several new digital forensic techniques to detect evidence of editing in digital multimedia content. We use Optical flow, DWT and different filters for forensic tasks such as identifying cut-and-paste forgeries from JPEG compressed videos and SIFT. This SIFT based technique is dependent on feature extraction by using key point detection. This method is mostly used to Detection of malicious manipulation with digital videos (digital forgeries) in case of copy-move attack. The proposed work has been found effective result as comparison to exiting model. In exiting model 98.2143 precision value is calculated while in proposed model we get the value of precision 99.2454. The proposed model get more forgery frame as compared to exiting model. These calculations are not a similar because the previously estimated calculation within the approach that they are connected on the complete frame to concentrate highlights rather than separating the frame into the squares. From the above forgery problem resolving we are using MATLAB toolbox and we are getting the 97 % accuracy of the work.