A EFFCIET DEEP FAKE FACE DETECTION USING DEEP INCEPTION NET LEARNING ALGORITHM
Main Article Content
Abstract
A Deep Fake Is Digital Manipulation Techniques That Use Deep Learning to Produce Deep Fake (Misleading) Images and Videos. Identifying Deep Fake Images Is the Most Difficult Part of Finding the Original. Due To the Increasing Reputation of Deep Fakes, Identifying Original Images and Videos Is More Crucial to Detect Manipulated Videos. This Paper Studies and Experiments with Different Methods That Can Be Used to Detect Fake and Real Images and Videos. The Convolutional Neural Network (Cnn) Algorithm Named Inception Net Has Been Used to Identify Deep Fakes. A Comparative Analysis Was Performed in This Work Based on Various Convolutional Networks. This Work Uses the Dataset from Kaggle With 401 Videos of Train Sample And 3745 Images Were Generated by Augmentation Process. The Results Were Evaluated with The Metrics Like Accuracy and Confusion Matrix. The Results of The Proposed Model Produces Better Results in Terms of Accuracy With 93% On Identifying Deep Fake Images and Videos.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Zhang, K., Zhang, Z., Li, Z., &Qiao, Y. (2016). Joint Face Detection And Alignment Using Multitask Cascaded
Convolutional Networks. Ieee Signal Processing Letters, 23(10), 1499-1503.
Mordvintsev, Alexander, Christopher Olah, And Mike Tyka. "Inceptionism: Going Deeper Into Neural Networks."
(2015).
Badale, Anuj, Et Al. "Deepfake Detection Using Neural Networks." 15th Ieee International Conference On
Advanced Video And Signal-Based Surveillance (Avss). 2018.
Dosovitskiy, Alexey, Et Al. "An Image Is Worth 16x16 Words: Transformers For Image Recognition At Scale."
Arxiv Preprint Arxiv:2010.11929 (2020).
Bayar, Belhassen, And Matthew C. Stamm. "A Deep Learning Approach To Universal Image Manipulation
Detection Using A New Convolutional Layer." Proceedings Of The 4th Acm Workshop On Information Hiding And
Multimedia Security. 2016.
Ioffe, S., & Szegedy, C. (2015, June). Batch Normalization: Accelerating Deep Network Training By Reducing
Internal Covariate Shift. In International Conference On Machine Learning (Pp. 448-456). Pmlr.
Chen, Chun-Fu Richard, Quanfu Fan, And Rameswar Panda. "Crossvit: Cross-Attention Multi-Scale Vision
Transformer For Image Classification." Proceedings Of The Ieee/Cvf International Conference On Computer Vision.
Heo, Young-Jin, Et Al. "Deepfake Detection Scheme Based On Vision Transformer And Distillation." Arxiv Preprint
Arxiv:2104.01353 (2021).
Zhang, Kaipeng, Et Al. "Joint Face Detection And Alignment Using Multitask Cascaded Convolutional Networks."
Ieee Signal Processing Letters 23.10 (2016): 1499-1503.,
Kaggle, Https://Www.Kaggle.Com/Competitions/Deepfake-Detectionchallenge/Data