Abstract
Machine learning and artificial intelligence in Journalism are aid and not a replacement or challenge to a journalist’s ability. Artificial intelligence-backed fake news characterized by misinformation and disinformation is the new emerging threat in our broken information ecosystem. Deepfakes erode trust in visual evidence, making it increasingly challenging to discern real from fake. Deepfakes are an increasing cause for concern since they can be used to propagate false information, fabricate news, or deceive people. While Artificial intelligence is used to create deepfakes, the same technology is also used to detect them. Digital Media literacy, along with technological deepfake detection tools, is an effective solution to the menace of deepfake. The paper reviews the creation and detection of deepfakes using machine learning and deep learning models. It also discusses the implications of cognitive biases and social identity theories in deepfake creation and strategies for establishing a trustworthy information ecosystem. The researchers have developed a prototype deepfake detection model, which can lay a foundation to expose deepfake videos. The prototype model correctly identified 35 out of 50 deepfake videos, achieving 70% accuracy. The researcher considers 65% and above as “fake” and 65% and below as “real”. 15 videos were incorrectly classified as real, potentially due to model limitations and the quality of the deepfakes. These deepfakes were highly convincing and flawless. Deepfakes have a high potential to damage reputations and are often obscene or vulgar. There is no specific law for deepfakes, but general laws require offensive/fake content to be taken down. Deepfakes are often used to spread misinformation or harm someone’s reputation. They are designed to harass, intimidate, or spread fear. A significant majority of deepfake videos are pornographic and target female celebrities
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.