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RETRACTED ARTICLE: Deepfake detection using rationale-augmented convolutional neural network

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This article was retracted on 10 January 2024

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Abstract

Deepfake network is a prominent topic of research as an application to various systems about security measures. Although there have been many recent advancements in facial reconstruction, the greatest challenge to overcome has been the means of finding an efficient and quick way to compute facial similarities or matches. This work is created utilizing the rationale-augmented convolutional neural network (CNN) on MATLAB R2019a platform using the Kaggle DeepFake Video dataset with an accuracy of 95.77%. Hence, real-time deepfake facial reconstruction for security purposes is difficult to complete concerning limited hardware and efficiency. This research paper looks into rational augmented CNN state-of-the-art technology utilized for deepfake facial reconstruction via hardware such as webcams and security cameras in real time. Additionally, discuss a history of face reconstruction and provide an overview of how it is accomplished.

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References

  • Abu Rumman A (2021) Transformational leadership and human capital within the disruptive business environment of academia. WJET 13(2):178–187. https://doi.org/10.18844/wjet.v13i2.5652

    Article  Google Scholar 

  • Abu-Rumman A, Al Shraah A, Al-Madi F et al (2021) Entrepreneurial networks, entrepreneurial orientation, and performance of small and medium enterprises: are dynamic capabilities the missing link? J Innov Entrep 10:29. https://doi.org/10.1186/s13731-021-00170-8

    Article  Google Scholar 

  • Agarwal S, Varshney LR (2019) Limits of deepfake detection: a robust estimation viewpoint. arXiv preprint. arXiv:1905.03493

  • Alhayani B, Abdallah AA (2020) Manufacturing intelligent Corvus corone module for a secured two way image transmission under WSN. EC Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/EC-02-2020-0107

  • Alhayani BSA, llhan H, (2021) Visual sensor intelligent module based image transmission in industrial manufacturing for monitoring and manipulation problems. J Intell Manuf 32:597–610. https://doi.org/10.1007/s10845-020-01590-1

    Article  Google Scholar 

  • Alhayani B, Abbas ST, Mohammed HJ et al (2021) Intelligent secured two-way image transmission using corvus corone module over WSN. Wireless Pers Commun. https://doi.org/10.1007/s11277-021-08484-2

    Article  Google Scholar 

  • Al-Hayani B, Ilhan H (2020) Efficient cooperative image transmission in one-way multi-hop sensor network. Int J Electr Eng Educ 57(4):321–339

    Article  Google Scholar 

  • Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In Proceedings of the International Conference on Machine Learning, Sydney, 6–11 August 2017, 214–223.

  • Arjovsky M, Chintala S, Bottou L (2017) Wasserstein generative adversarial networks. In International Conference on Machine Learning, 214–223

  • Bai S (2017) Growing random forest on deep convolutional neural networks for scene categorization. Expert Syst Appl 71:279–287

    Article  Google Scholar 

  • Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security. ACM, 5–10

  • Chollet, F (2017) Xception: deep learning with depthwise separable convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, 21–26 July 2017, 1610–2357

  • Dang L, Hassan S, Im S, Lee J, Lee S, Moon H (2018) Deep learning based computer generated face identification using convolutional neural network. Appl Sci 8:2610

    Article  Google Scholar 

  • Gulrajani I, Ahmed F, Arjovsky M, Dumoulin V, Courville AC (2017) Improved training of Wasserstein GANs. In Advances in Neural Information Processing Systems, 5767–5777

  • Hsu C-C, Zhuang Y-X, Lee C-Y (2020) Deep fake image detection based on pairwise learning. Appl Sci 10(1):370. https://doi.org/10.3390/app10010370

    Article  Google Scholar 

  • Huang G, Liu Z, Van Der Maaten L, Weinberger K (2017) Densely connected convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, 4700–4708

  • Huaxiao Mo BC, Luo W (2018) Fake Faces Identification via Convolutional Neural Network. In Proceedings of the ACM Workshop on Information Hiding and Multimedia Security, Innsbruck, Austria, 20–22 June 2018, 43–47

  • Kaggle (2021) Available Online: https://www.kaggle.com/c/deepfake-detection-challenge/data

  • Keren G, Deng J, Pohjalainen J (2016) Convolutional Neural Networks with Data Augmentation for Classifying Speakers’ Native Language, Chair of Complex & Intelligent Systems, University of Passau, Germany Department of Computing, Imperial College London, UK

  • Korshunova I, Shi W, Dambre J, Theis L (2017) Fast face-swap using convolutional neural networks. In Proceedings of the IEEE International Conference on Computer Vision, 3677–3685

  • Kwekha-Rashid AS, Abduljabbar HN, Alhayani B (2021) Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Appl Nanosci. https://doi.org/10.1007/s13204-021-01868-7

    Article  Google Scholar 

  • Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC (2016) Ssd: Single shot multibox detector. In European Conference on Computer Vision; Springer: Cham, Switzerland. pp. 21–37

  • Mao X, Li Q, Xie H, Lau RY, Wang Z, Smolley SP (2017a) Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 22–29 October 2017, 2813–2821

  • Mao, X., Li, Q., Xie, H., Lau, R. Y., Wang, Z., and Paul Smolley, S. (2017b). Least squares generative adversarial networks. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2794–2802).

  • Miyato T, Kataoka T, Koyama M, Yoshida Y (2018) Spectral normalization for generative adversarial networks. In Proceedings of the International Conference on Learning Representations, Vancouver, 30 April–3 May 2018

  • Qian Y, Dong J, Wang W, Tan T (2015) Deep learning for stage analysis via convolutional neural networks. In Media Watermarking, Security, and Forensics 2015 9409: 94090J

  • Radford A, Metz L, Chintala S (2015) Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv 2015. arXiv:1511.06434

  • Rashid AS, Tout K, Yakan A (2021) The critical human behavior factors and their impact on knowledge management system–cycles. Business Process Management Journal, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/BPMJ-11-2020-0508

  • Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M et al (2015) ImageNet large scale visual recognition challenge. Int J Comput vis 115:211–252

    Article  Google Scholar 

  • Sun Y et al (2014) Deep Learning Face Representation from Predicting 10,000 Classes. Obtained From: http://mmlab.ie.cuhk.edu.hk/pdf/YiSun_CVPR14.pdf

  • Wang X, Thome N, Cord M (2017) Gaze latent support vector machine for image classification improved by weakly supervised region selection. Pattern Recogn 72:59–71

    Article  Google Scholar 

  • Wen D, Han H, Jain AK (2015) Face spoof detection with image distortion analysis. IEEE Trans Inf Forensics Secur 10(4):746–761

    Article  Google Scholar 

  • Wubet WM (2020) The Deepfake Challenges and Deepfake Video Detection. International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278–3075, Volume-9 Issue-6. https://doi.org/10.35940/ijitee. E2779.049620

  • Xuan X, Peng B, Dong J, Wang W (2019) On the generalization of GAN image forensics. arXiv preprint. arXiv:1902.11153

  • Yahya W, Ziming K, Juan W et al (2021) Study the influence of using guide vanes blades on the performance of cross-flow wind turbine. Appl Nanosci. https://doi.org/10.1007/s13204-021-01918-0

    Article  Google Scholar 

  • Yang P, Ni R, Zhao Y (2016) Recapture image forensics based on Laplacian convolutional neural networks. In International Workshop on Digital Watermarking. Springer, Charm, 119–128

  • Zheng L, Duffner S, Idrissi K, Garcia C, Baskurt A (2016) Siamese multi-layer perceptrons for dimensionality reduction and face identification. Multimed Tools Appl 75(9):5055–5073

    Article  Google Scholar 

  • Zhang Y, Zheng L, Thing VL (2017) Automated face swapping and its detection. In 2017 IEEE 2nd International Conference on Signal and Image Processing (ICSIP), 15–19. IEEE

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This study was self-funded.

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Authors and Affiliations

  1. Department of Computer Engineering, Karabuk University, Karabuk, Turkey

    Saadaldeen Rashid Ahmed Ahmed & Emrullah Sonuç

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  1. Saadaldeen Rashid Ahmed Ahmed
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  2. Emrullah Sonuç
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Correspondence to Saadaldeen Rashid Ahmed Ahmed.

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This article has been retracted. Please see the retraction notice for more detail:https://doi.org/10.1007/s13204-024-03022-5

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Ahmed, S.R.A., Sonuç, E. RETRACTED ARTICLE: Deepfake detection using rationale-augmented convolutional neural network. Appl Nanosci 13, 1485–1493 (2023). https://doi.org/10.1007/s13204-021-02072-3

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  • DOI: https://doi.org/10.1007/s13204-021-02072-3

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