Deep Fake Detection using LSTM and Survey of Deep Fake Creation Technologies
Keywords:
Convolutional Neural Networks; Deep Fake; Deep Learning; GAN; LSTMAbstract
Artificial intelligence known as Deep Fake is one of many techniques that have been successfully developed in recent years for altering faces in images and videos. It can produce convincingly faked images, audio, and video. Deep Fake can create problems, especially when there is a media component involved. Even if it is helpful, when it is used maliciously, such as for disseminating fake news or cyberbullying, it can pose a threat to society. It is necessary to develop a complete fake detection method to handle such issues. Too far, numerous methods have been developed to distinguish between authentic and fraudulent videos. The objective of this work is to give a summary of different approaches for Deep Fake creation and to provide an overview of LSTM algorithms for deep fake video detection.
Downloads
References
“Deep Fakes Web," [Online]. Available:https://deepfakesweb.com/. [Accessed 15 May 2022].
Y. Li, X. Yang, P. Sun, H. Qi and S. Lyu, "Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics," in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020.
"Face App," [Online]. Available:https://www.faceapp.com/. [Accessed 15 May 2022].
"Wombo," [Online]. Available:https://www.wombo.ai/. [Accessed 15 May 2022].
Y. Pashaeva, "Scammers Are Using Deepfake Videos Now," Slate, 13 September 2021. [Online]. Available: https://slate.com/technology/2021/09/deepfake-video-scams.html. [Accessed 15 May 2022].
Chesney, Robert and Citron, Danielle Keats, DeepFakes: A Looming Challenge for Privacy, Democracy,and National Security (July 14, 2018). 107 California Law Review (2019, Forthcoming); U of Texas Law,Public Law Research Paper No. 692; U of Maryland Legal Studies Research Paper No. 2018-21.
Jessica and Silbey Woodrow Hartzog , “The Upside of Deep Fakes”, Maryland Law Review, Volume 78 issue 4,2019
Schwartz, Oscar (12 November 2018). "You thought fake news was bad? Deep fakes are where the truth goes to die". The Guardian.Sik-Ho Tsang, “Review: Inception-v3 — 1st Runner Up (Image Classification) in ILSVRC 2015” ,https://medium.com/@sh.tsang/review-inception-v3-1st-runner-up-image-classification-in-ilsvrc-2015-17915421f77c.
A Voice Deepfake Was Used To Scam A CEO Out Of $243,000 https://www.forbes.com/sites/jessedamiani/2019/09/03/a-voice-deepfake-was-used-to-scam-a-ceo-out-of-243000/#31bb21a32241, September 3, 2019
Josh Horwitz, https://www.reuters.com/article/us-china-tech-zao-idUSKCN1VN0G9.
Momina Masood1 & Mariam Nawaz & Khalid Mahmood Malik & Ali Javed & Aun Irtaza & Hafiz Malik.”Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward.” 11 May 2022
Thanh Thi Nguyen, Quoc Viet Hung Nguyen, Cuong M. Nguyen, Dung Nguyen, Duc Thanh Nguyen, Saeid Nahavandi ,”Deep Learning for Deep Fakes Creation and Detection: A Survey”,26 April 2021.
Remya Revi K.1 , Vidya K. R.1 and M. Wilscy,” Detection of Deepfake Images Created Using Generative Adversarial Networks –A Review “, February 2021
PavelKorshunov, Sebastien Marcel, “DeepFakes: a New Threat to Face Recognition? Assessment and Detection”, citing arXiv: 1812.08685[cs.CV], 20 Dec 2018.
M. Grothaus, "Deepfake videos are the latest cruel form of school bullying – parents and teachers must watch out," iNews UK, 9 November 2021.[Online]. Available:https://inews.co.uk/news/technology/deepfake-videos-school-bullying-cyberbullying-ai-apps-parents-teachers-children-1290664. [Accessed 15 May 2022].
Crescendo, "YouTube," 16 April 2020. [Online].Available:https://www.youtube.com/watch?v=xkqflKC64IM [Accessed 02 April 2022].
A. Jadhav, A. Patange, J. Patel, H. Patil and M.Mahajan, "Deepfake Video Detection Using Neural Networks," International Journal for Scientific Research and Development, vol. 8, no.1, 2020.
Abbreviated View of Deepfake Videos Detection Techniques, Mohammed A. Younus ,Taha M. Hasan.6 th international engineering conference" Sustainable Technology and Development” , ( IEC-2020), Erbil , Iraq
N. Bonettini, E. D. Cannas, S. Mandelli, L. Bondi,P. Bestagini and S. Tubaro, "Video Face Manipulation Detection Through Ensemble of CNNs," in 2020 25th International Conference on Pattern Recognition (ICPR), 2021.
D. Güera and E. J. Delp, "Deepfake Video Detection Using Recurrent Neural Networks," in 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2018.
Yuezun Li, and SiweiLyu, “Exposing DeepFake Videos By Detecting Face Warping Artifacts”,arXiv:1811.00656v3 [cs.CV] 22 May 2019 https://doi.org/10.1016/S0969-4765(19)30137-7.
FalkoMatern , Christian Riess and Marc Stamminger,“Exploiting Visual Artifacts to Expose Deepfakes and Face Manipulations ”, 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW) https://doi.org/10.1109/WACVW.2019.00020
DariusAfchar, Vincent Nozick, Junichi Yamagishi and Isao Echizen, “MesoNet: a Compact Facial Video Forgery Detection Network”, arXiv:1809.00888v1[cs.CV] 4 Sep 2018 https://doi.org/10.1109/WIFS.2018.8630761
A. Rossler, D. Cozzolino, L. Verdoliva, C. Riess,J. Thies and M. Nießner, "FaceForensics++:Learning to Detect Manipulated Facial Images," in IEEE/CVF International Conference on Computer Vision, 2019.
A. Karandikar, V. Deshpande, S. Singh, S.Nagbhidkar and S. Agarwal, "Deepfake VideoDetection Using Convolutional Neural Network,"International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, 2020.
K. Simonyan and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," in arXiv 10.48550/ARXIV.1409.1556, 2014.
Xin Yang ,Yuezun Li and SiweiLyu, “Exposing Deep Fakes Using Inconsistent Head Poses” , ICASSP 2019 -2019 IEEE ICASSP,17 May 2019
A. Jadhav, A. Patange, J. Patel, H. Patil and M.Mahajan, "Deepfake Video Detection Using Neural Networks," International Journal for Scientific Research and Development, vol. 8, no.1, 2020.
Yuezun Li, Ming-Ching Chang and SiweiLyu,In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking, arXiv:1806.02877v2 [cs.CV] 11Jun 2018.
A. Chintha et al., "Recurrent Convolutional Structures for Audio Spoof and Video Deepfake Detection," in IEEE Journal of Selected Topics in Signal Processing, vol. 14, no. 5, pp. 1024-1037, Aug. 2020, doi: 10.1109/JSTSP.2020.2999185.
A. A. Pokroy and A. D. Egorov, "EfficientNets for DeepFake Detection: Comparison of Pretrained Models," in IEEE Conference of Russian Young. Researchers in Electrical and Electronic Engineering, 2021 .
M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," in International Conference on Machine Learning, 2019.
Nguyen, H.H., Yamagishi, J., Echizen, I. “Capsule-Forensics Networks for Deepfake Detection”, Handbook of Digital Face Manipulation and Detection. Advances in Computer Vision and Pattern Recognition. Springer, Cham, 2022
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
