Deepfake Detection Using Resnext50
DOI:
https://doi.org/10.37591/jons.v10i1.917Keywords:
AI-Based algorithm, Deepfake detection, Artificial Intelligence, image patterns, algorithmic detection, AI-based toolsAbstract
Due to the recent advancements in Artificial Intelligence (AI), technologies it has led to the rapid development in the modification of audio, video, and image manipulation techniques. This AIgenerated media content is referred to as "Deep Fakes". AI-based tools can exploit and manipulate media in increasingly believable ways like super-imposing a person’s face on another person’s body or creating copies of a public person’s voice. A range of papers has discovered significant signs of deep fakes, including unnatural blinking patterns, inconsistent head poses, distortion in facial features, incongruities between the speech and mouth movements, and even learning to note the absence. Deepfake detection solutions use multimodal detection techniques to determine whether target media has been manipulated or generated artificially. Existing detection methods are classified into two types: manual and algorithmic methods. Human media forensic practitioners, often armed with software tools, use manual techniques. Algorithmic detection identifies manipulated media using an AI-based algorithm. Our Deepfake detection system was created to identify deep fakes and manipulations in images and videos.
References
Thanh, Ankit Parekh, Faruk Kazi. Explainable Deep-Fake Detection Using Visual Interpretability Methods. 2020 3rd International Conference on Information and Computer Technologies (ICICT). 2020; 289–293.
Xin Yang, Yuezun Li, Siwei Lyu. Exposing Deep Fakes using Inconsistent Head Poses. ICASSP 2019, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2019; 8261–8265.
Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner. FaceForensics++: Learning to Detect Manipulated Facial Images. arXiv:1901.08971 [cs.CV]. 2019; 1–11.
Ruben Tolosana, Ruben Vera-Rodriguez, Julian Fierrez, Aythami Morales, Javier Ortega-Garcia. DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection. Inf Fusion. 2020; 64: 131–148.
Zahid Akhtar, Dipankar Dasgupta. A Comparative Evaluation of Local Feature Descriptors for DeepFakes Detection. 2019 IEEE International Symposium on Technologies for Homeland Security (HST). 2019; 1–5.
Siwei Lyu. Deep Fake Detection: Current Challenges and Next Steps. 2020 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). 2020; 1–6.
Downloads
Published
Issue
Section
License
Declaration and Copyright Transfer Form
(to be completed by authors)
I/ We, the undersigned author(s) of the submitted manuscript, hereby declare, that the above manuscript which is submitted for publication in the STM Journals(s), is not published already in part or whole (except in the form of abstract) in any journal or magazine for private or public circulation, and, is not under consideration of publication elsewhere.
- I/We will not withdraw the manuscript after 1 week of submission as I have read the Author Guidelines and will adhere to the guidelines.
- I/We Author(s ) have niether given nor will give this manuscript elsewhere for publishing after submitting in STM Journal(s).
- I/ We have read the original version of the manuscript and am/ are responsible for the thought contents embodied in it. The work dealt in the manuscript is my/ our own, and my/ our individual contribution to this work is significant enough to qualify for authorship.
- I/We also agree to the authorship of the article in the following order:
Author’s name
1. ________________
2. ________________
3. ________________
4. ________________
| We Author(s) tick this box and would request you to consider it as our signature as we agree to the terms of this Copyright Notice, which will apply to this submission if and when it is published by this journal. |
