INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
In recent years, the widespread use of smartphones and social networks has made digital images and videos a common occurrence online. However, this increased usage has also led to a surge in techniques for altering image content, such as using editing software like Photoshop. Deepfake videos and images have emerged as a significant public concern. While technologies like Face Swap and deepfake have opened up new possibilities in various fields, they have also made it easier for malicious users to generate video forgeries.
Deepfake is an AI-based technique that superimposes existing images or videos onto different ones using neural networks. Unfortunately, deepfakes have been misused to spread misinformation, invade privacy, and deceive viewers through sophisticated algorithms and AI. This has become a nuisance on social media platforms, with fake videos merging a celebrity's face with explicit content. The impact of deepfakes is particularly alarming, with nefarious actors targeting politicians, senior corporate officers, and world leaders.
Our proposed approach focuses on detecting deepfake videos of politicians by analyzing temporal sequential frames. We extract frames from the forged videos and employ a deep depth-based convolutional long short-term memory model to identify fake frames. The effectiveness of our method is evaluated on a newly collected ground truth dataset of forged videos featuring source and destination frames of famous politicians. Experimental results demonstrate the efficacy of our approach in detecting deepfake videos.
"Deepfake detection through deep learning", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.i834-i840, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305897.pdf
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2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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