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In recent years, the proliferation of deepfake technology has raised significant concerns regarding the authenticity and trustworthiness of digital media. Deepfakes, which are highly realistic manipulations of audio and visual content created using artificial intelligence techniques, pose a serious threat to various aspects of society, including misinformation, privacy infringement, and cybersecurity breaches. Detecting and mitigating the spread of deepfakes has become a critical priority for researchers and practitioners in the field of computer vision and artificial intelligence. In this context, the development of advanced deep learning models for deepfake detection has emerged as a promising approach to address this pressing challenge.This research develops an advanced deep learning model to identify modified material in order to combat the rising issue of fraudulent videos. The process entails importing datasets, removing frames from films, locating landmarks on the face, and examining the frames to find abnormalities typical of deep fakes. There are several CNN architectures used, including ResNet-50 and EfficientNetB2. CNN and sequential architectures (e.g., InceptionV3 + GRU, EfficientNetB2 + GRU) are effective. The litmus test for the efficacy of our model lies in its evaluation using the Deep Fake Detection Challenge (DFDC) dataset—an industry benchmark recognized for gauging the prowess of deepfake detection systems. In addition to model refinement, we proffer a proactive strategy to counteract the pervasive dissemination of deepfakes.
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Copyright © 2024, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: D. Ramachandra Reddy, ramchandra.reddy60@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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