[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116946-en":3,"doc-seo-116946-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116946,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Broadcasting Forensics Using Machine Learning Approaches - Abstract","Broadcasting forensics applies scientific methods to analyze and authenticate multimedia content, especially amid the rapid growth of consumer-grade imaging sensors and the resulting volume of images and videos used for public and private communication. Many visual materials can be manipulated to misrepresent facts and mislead audiences. The work proposes a set of forensics and counter-forensic techniques to support content authenticity and integrity while also exploring intentional modifications to mislead adversaries. Public datasets and organized challenges evaluate the approach, showing accurate identification of manipulated content, restoration of original media, and deceptive performance versus state-of-the-art forensic methods.","Broadcasting Forensics Using Machine Learning Approaches  \nAmit Kapoor1, Prof. Vinod Mahor2  \n1M Tech Scholar, 2Assistant Professor,  \n1,2Computer Science & Engineering, Millennium Institute of Technology & Science, Bhopal, Madhya Pradesh, India  \nABSTRACT  \nHow to cite this paper: Amit Kapoor | Prof. Vinod Mahor \"Broadcasting Forensics Using Machine Learning Approaches\"  \nPublished in  \nInternational Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456- 6470, Volume-7 | Issue-3, June 2023, pp.1034-1045, URL: [www.ijtsrd.com/papers/ijtsrd57545.pdf](www.ijtsrd.com/papers/ijtsrd57545.pdf)  \nBroadcasting 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  \ncan misrepresent the facts and deceive viewers. To address this issue,  \nundergo modification and manipulation before public release, which  \nwe propose a set of forensics and counter-forensic techniques that  \nCopyright © 2023 by author (s) and International Journal of Trend in Scientific Research and Development Journal. This is an  \nOpen Access article distributed under the  \nterms of the Creative Commons Attribution License (CC BY 4.0)([http://creativecommons.org/licenses/by/4.0](http://creativecommons.org/licenses/by/4.0))  \ncan help establish the authenticity and integrity of Multimedia  \ncontent. Additionally, we suggest ways to modify the content  \nintentionally to mislead potential adversaries. Our proposed tools are  \nevaluated using publicly available datasets and independently  \norganized challenges. Our results show that the forensics and  \ncounter-forensic techniques can accurately identify manipulated  \ncontent and can help restore the original image or video.  \nFurthermore, in this paper demonstrate that the modified content can  \nsuccessfully deceive potential adversaries while remaining  \nundetected by state-of-the-art forensic methods.  \nKEYWORDS: Broadcasting Forensics, Multimedia Content Analysis, Image and Video Manipulation Authenticity and Integrity, Forensicsand Counter-Forensic Techniques, ML  \n1. INTRODUCTION   \nThe widespread use of smartphones has news\" problem [1] and the recent rise in popularity of  \nfundamentally altered how we communicate with one another. Unprecedented levels of digital content creation, such as photographs and films, are now possible because to these gadgets. The gold standard for storing our memories or indicating our participation at social occasions is currently thought to be digital information. Numerous pieces of software have been created in order to edit and improve these digital assets because of the value placed on images and videos as well as their significant role in social networks and journalistic Broadcasting. This software innovation has a cost, though. Image and video manipulation, whether done for aesthetic or malevolent reasons, is becoming a popular practise. The public is starting to reject taking any image or video as digital evidence because of the abundance of these faked Broadcasting. One of the first to make a suggestion of this pattern. The \"fake  \nmachine learning-based techniques like generative adversarial networks that may be abused by bad actors are also aggravating this scepticism.  \nThe research community has been creating methods to check and authenticate such content in order to reduce the spread of false information and assist law enforcement authorities with the completion of investigations in which digital assets may be involved. These initiatives gave rise to the are","cbCaiozbWUsliq0L","https://ap.wps.com/l/cbCaiozbWUsliq0L","pdf",1900564,1,12,"English","en",105,"# Abstract\n# Introduction\n## Source Image Forensics\n## Content-Based Forensics\n## Source Camera Identification with CNN","[{\"question\":\"What is broadcasting forensics in this paper?\",\"answer\":\"Broadcasting forensics uses scientific methods to analyze and authenticate multimedia content, including establishing authenticity and integrity of images and videos.\"},{\"question\":\"How does the paper address manipulated images and videos?\",\"answer\":\"It proposes forensics and counter-forensic techniques to identify manipulation, help restore original media, and also study intentional modifications that can mislead adversaries.\"},{\"question\":\"How are the proposed tools evaluated?\",\"answer\":\"The tools are evaluated using publicly available datasets and independently organized challenges, with results indicating accurate detection of manipulated content and deception against state-of-the-art methods.\"}]","Broadcasting Forensics Using Machine Learning Approaches - 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