Broadcasting Forensics Using Machine Learning Approaches

Broadcasting forensic is the practice of using scientific methods and techniques to analyse and authenticate Multimedia content. Over the past decade, consumer grade imaging sensors have become increasingly prevalent, generating vast quantities of images and videos that are used for various public and private communication purposes. Such applications include publicity, advocacy, disinformation, and deception, among others. This paper aims to develop tools that can extract knowledge from these visuals and comprehend their provenance. However, many images and videos undergo modification and manipulation before public release, which can misrepresent the facts and deceive viewers. To address this issue, we propose a set of forensics and counter forensic techniques that can help establish the authenticity and integrity of Multimedia content. Additionally, we suggest ways to modify the content intentionally to mislead potential adversaries. Our proposed tools are evaluated using publicly available datasets and independently organized challenges. Our results show that the forensics and counter forensic techniques can accurately identify manipulated content and can help restore the original image or video. Furthermore, in this paper demonstrate that the modified content can successfully deceive potential adversaries while remaining undetected by state of the art forensic methods. Amit Kapoor | Prof. Vinod Mahor "Broadcasting Forensics Using Machine Learning Approaches" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57545.pdf Paper URL: https://www.ijtsrd.com.com/engineering/computer-engineering/57545/broadcasting-forensics-using-machine-learning-approaches/amit-kapoor