Published Paper Details:

Forensic video/Image analysis techniques are used many times to perform visual data analysis. Video/Image forensics analysis is considered to be important to show that images and videos which are to be used as potential evidence in court of law are verifiably true / authentic. The popularity of digital devices such as smart mobile devices and also due to increasing number of low cost surveillance systems, several forms of visual data are widely being used in digital forensic investigation. Digital videos are used most of the times as key evidence sources in evidence identification, analysis, presentation, and report. A Deep Learning based forensic video/image analysis framework can be developed that employs an efficient video/image processing techniques using deep learning, such as object detection framework YOLO V3, CNN based summarization of videos, enhancing algorithm for the low quality of video footage analysis which consists of adaptive video enhancement algorithm based on Contrast Adaptive Histogram Equalization (CLAHE) technique, Contrast Exposure Fusion Algorithm, Dynamic Histogram Equalization (DHE) which are termed to be useful in order to improve the quality of visual data for the use of digital forensic identification & investigation. The technique of Video/Image tampering detection using state-of-art techniques and textual enhancement are also termed to be important in order to assist in truthful analysis of visual data evidence. The framework would deploy recent techniques and algorithms which will assist examiner to perform visual analysis of evidence data.