International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 04 | Apr 2022
p-ISSN: 2395-0072
www.irjet.net
Fake News Analyzer Nitin Sharma Student, Dept. Electronics & Telecommunications, K. J. Somaiya Institute Of Engineering & Technology, Sion, Mumbai, India
Ritik Sharma Student, Dept. Electronics & Telecommunications, K. J. Somaiya Institute Of Engineering & Technology, Sion, Mumbai, India
Sharan Shetty Student, Dept. Electronics & Telecommunications, K. J. Somaiya Institute Of Engineering & Technology, Sion, Mumbai, India
Prof. Harshwardhan Ahire Assistant Professor, Dept. Electronics & Telecommunications, K. J. Somaiya Institute Of Engineering & Technology, Sion, Mumbai, India ---------------------------------------------------------------------***--------------------------------------------------------------------which come along with all the pleasures of inventions. The major concern for this generation is to educate the misinformation in news content and face tempering in citizens about the consumption of content. What we videos. The system is broken down into three see, read, and listen to on the pool of the internet segments. 1. A simple UI’s designed for the users to impacts the way we see things around us and also our insert alleged text, paragraph, headline or statement to perspectives. These very contents are designed to analyses and process it.2.Text based misinformation deliberately mislead masses about certain subjects and detection; the system would run a text classifier once a to induce a very specific view about them. These sentence, statement, or paragraph has been inserted contents could be false, intentionally plotted, into the system in text format. Datasets like LIAR (with misreported, polarized, persuasive information, and over 12.8k manually labeled short statements were citizen journalism with improper facts. Hence, a collected) and different datasets from Kaggle were system/ application is needed that would identify and used for training the model. Sentimental analysis was break down any alleged information on the basis of executed after cleaning, lemmatizing, and splitting the detailed analysis. This paper tries to present a case for text data. The text than is processed on the basis of an ML-based application that would use a simple UI for probability of truth or false it propagates. 3. Deep fake users to insert or upload their query text, paragraph, detection; Deep-Fake and Face2Face were used to headline, statement or alleged video to process it for build a model that could automatically and efficiently fake content analysis and detect its authenticity. This detect forged faces in a video. It follows a deep learning would deal with Deep fakes and misinformation in the approach to detect whether the given video is text format. The system is divided into two different subjected to forgery or not. The whole system is segments that would focus on two different mediums vertical integration of analyzing different aspects that of spreading fake news i.e. Video, and text. Videos are contribute to misinformation and fake data (in the subjected to deep fakes and wrongful captions which form of images, videos, and texts). The system would then are forwarded and reposted so many times that it contribute to fighting fake news. becomes difficult to verify their true source. We 1. Introduction worked on a deep fake and an object detection model that would help us to understand whether a video is With the advancements in the field of technology and face forged or not. And notify its true source. We have the increase in the number of people using those used a microscopic level of analysis that would help us technologies, there tends to have certain repercussions in detecting face forgery which is hard to identify from
Abstract - This paper presents a method to detect
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