Deep fake detection using deep learning techniques

Research Article
Open access

Deep fake detection using deep learning techniques

Gayathri S. 1* , Santhiya S. 2 , Nowneesh T. 3 , Sanjana Shuruthy K. 4 , Sakthi S. 5
  • 1 Computer Science and Engineering, Kongu Engineering College, Erode, India    
  • 2 Artificial Intelligence, Kongu Engineering College, Erode, India    
  • 3 Computer Science and Engineering, Kongu Engineering College, Erode, India    
  • 4 Computer Science and Engineering, Kongu Engineering College, Erode, India    
  • 5 Computer Science and Engineering, Kongu Engineering College, Erode, India    
  • *corresponding author sgayathricse97@gmail.com
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220655
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

Deep fake is the artificial manipulation and creation of data, primarily through photo-graphs or videos into the likeness of another person. This technology has a variety of ap-plications. Despite its uses, it can also influence society in a controversial way like de-faming a person, Political distress, etc. Many models had been proposed by different re-searchers which give an average accuracy of 90%. To improve the detection efficiency, this proposed paper uses 3 different deep learning techniques: Inception ResNetV2, Effi-cientNet, and VGG16. These proposed models are trained by the combination of Facfo-rensic++ and DeepFake Detection Challenge Dataset. This proposed system gives the highest accuracy of 97%.

Keywords:

deepfake detection, inception resNetv2, EfficientNet B4, VGG16, FaceForensic++, DeepFake Detection Challenge Dataset

S.,G.;S.,S.;T.,N.;K.,S.S.;S.,S. (2023). Deep fake detection using deep learning techniques. Applied and Computational Engineering,2,232-241.
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References

[1]. Ahmed, S. R. A., & Sonuç, E., ‘‘Deepfake detection using rationale-augmented convolutional neural network”, Applied Nanoscience, pp. 1-9. (2021).

[2]. Amerini, Irene, et al. ‘‘Deepfake video detection through optical flow based cnn. ’’ Proceedings of the IEEE/CVF international conference on computer vision workshops. (2019).

[3]. Chang, Xu, Jian Wu, Tongfeng Yang, and Guorui Feng. ‘‘Deepfake face image detection based on improved VGG convolutional neural network. ’’In 2020 39th chinese control conference (CCC), IEEE, pp. 7252-7256. (2020).

[4]. Hsu, Chih-Chung, Yi-Xiu Zhuang, and Chia-Yen Lee. ‘‘Deep fake image detection based on pairwise learning. ’’Applied Sciences vol. 10, no. 1, pp. 370. (2020).

[5]. Guarnera, Luca, Oliver Giudice, and Sebastiano Battiato. ‘‘Deepfake detection by analyzing convolutional traces. ’’ In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 666-667. (2020).

[6]. Güera, David, and Edward J. Delp. ‘‘Deepfake video detection using recurrent neural networks. ’’ In 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, pp. 1-6. (2018).

[7]. Ismail, Aya, Marwa Elpeltagy, Mervat S. Zaki, and Kamal Eldahshan. ‘‘ A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost.’’ Sensors vol. 21, no. 16, pp. 5413. (2021).

[8]. Jung, Tackhyun, Sangwon Kim, and Keecheon Kim. ‘‘Deepvision: Deepfakes detection using human eye blinking pattern.’’ IEEE Access 8, pp. 83144-83154. (2020).

[9]. Li, Yuezun, and Siwei Lyu. ‘‘ Exposing deepfake videos by detecting face warping artifacts. ’’ arXiv preprint arXiv:1811.00656, (2018).

[10]. Malolan, Badhrinarayan, Ankit Parekh, and Faruk Kazi. ‘‘Explainable deep-fake detection using visual interpretability methods. ’’ In 2020 3rd International Conference on Information and Computer Technologies (ICICT), IEEE, pp. 289-293. (2020).

[11]. Mitra, Alakananda, Saraju P. Mohanty, Peter Corcoran, and Elias Kougianos. ‘‘A machine learning based approach for deepfake detection in social media through key video frame extraction. ’’SN Computer Science 2, no. 2, pp. 1-18. (2021).

[12]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.

[13]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand prediction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.

[14]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.

[15]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.

[16]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.

[17]. Sathishkumar, V. E., Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, B. Venkatesh, and S. Santhoshkumar. "Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment." Arabian Journal for Science and Engineering (2021): 1-9.

[18]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

[19]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E., Visiting Indian Hospitals Before, During and After Covid. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (1), 111-123, 2022.

[20]. Easwaramoorthy, S., Moorthy, U., Kumar, C. A., Bhushan, S. B., & Sadagopan, V. (2017, January). Content based image retrieval with enhanced privacy in cloud using apache spark. In International Conference on Data Science Analytics and Applications (pp. 114-128). Springer, Singapore.

[21]. Sathishkumar, V. E., Agrawal, P., Park, J., & Cho, Y. (2020, April). Bike Sharing Demand Prediction Using Multiheaded Convolution Neural Networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 264-265). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[22]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2021). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems.

[23]. Babu, J. C., Kumar, M. S., Jayagopal, P., Sathishkumar, V. E., Rajendran, S., Kumar, S., ... & Mahseena, A. M. (2022). IoT-Based Intelligent System for Internal Crack Detection in Building Blocks. Journal of Nanomaterials, 2022.

[24]. Subramanian, M., Kumar, M. S., Sathishkumar, V. E., Prabhu, J., Karthick, A., Ganesh, S. S., & Meem, M. A. (2022). Diagnosis of retinal diseases based on Bayesian optimization deep learning network using optical coherence tomography images. Computational Intelligence and Neuroscience, 2022.

[25]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.

[26]. Sathishkumar, V. E., Rahman, A. B. M., Park, J., Shin, C., & Cho, Y. (2020, April). Using machine learning algorithms for fruit disease classification. In Basic & clinical pharmacology & toxicology (Vol. 126, pp. 253-253). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[27]. Sathishkumar, V. E., Venkatesan, S., Park, J., Shin, C., Kim, Y., & Cho, Y. (2020, April). Nutrient water supply prediction for fruit production in greenhouse environment using artificial neural networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 257-258). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[28]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[29]. Kotha, S. K., Rani, M. S., Subedi, B., Chunduru, A., Karrothu, A., Neupane, B., & Sathishkumar, V. E. (2021). A comprehensive review on secure data sharing in cloud environment. Wireless Personal Communications, 1-28.

Cite this article

S.,G.;S.,S.;T.,N.;K.,S.S.;S.,S. (2023). Deep fake detection using deep learning techniques. Applied and Computational Engineering,2,232-241.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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About volume

Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. Ahmed, S. R. A., & Sonuç, E., ‘‘Deepfake detection using rationale-augmented convolutional neural network”, Applied Nanoscience, pp. 1-9. (2021).

[2]. Amerini, Irene, et al. ‘‘Deepfake video detection through optical flow based cnn. ’’ Proceedings of the IEEE/CVF international conference on computer vision workshops. (2019).

[3]. Chang, Xu, Jian Wu, Tongfeng Yang, and Guorui Feng. ‘‘Deepfake face image detection based on improved VGG convolutional neural network. ’’In 2020 39th chinese control conference (CCC), IEEE, pp. 7252-7256. (2020).

[4]. Hsu, Chih-Chung, Yi-Xiu Zhuang, and Chia-Yen Lee. ‘‘Deep fake image detection based on pairwise learning. ’’Applied Sciences vol. 10, no. 1, pp. 370. (2020).

[5]. Guarnera, Luca, Oliver Giudice, and Sebastiano Battiato. ‘‘Deepfake detection by analyzing convolutional traces. ’’ In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp. 666-667. (2020).

[6]. Güera, David, and Edward J. Delp. ‘‘Deepfake video detection using recurrent neural networks. ’’ In 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS), IEEE, pp. 1-6. (2018).

[7]. Ismail, Aya, Marwa Elpeltagy, Mervat S. Zaki, and Kamal Eldahshan. ‘‘ A New Deep Learning-Based Methodology for Video Deepfake Detection Using XGBoost.’’ Sensors vol. 21, no. 16, pp. 5413. (2021).

[8]. Jung, Tackhyun, Sangwon Kim, and Keecheon Kim. ‘‘Deepvision: Deepfakes detection using human eye blinking pattern.’’ IEEE Access 8, pp. 83144-83154. (2020).

[9]. Li, Yuezun, and Siwei Lyu. ‘‘ Exposing deepfake videos by detecting face warping artifacts. ’’ arXiv preprint arXiv:1811.00656, (2018).

[10]. Malolan, Badhrinarayan, Ankit Parekh, and Faruk Kazi. ‘‘Explainable deep-fake detection using visual interpretability methods. ’’ In 2020 3rd International Conference on Information and Computer Technologies (ICICT), IEEE, pp. 289-293. (2020).

[11]. Mitra, Alakananda, Saraju P. Mohanty, Peter Corcoran, and Elias Kougianos. ‘‘A machine learning based approach for deepfake detection in social media through key video frame extraction. ’’SN Computer Science 2, no. 2, pp. 1-18. (2021).

[12]. Sathishkumar V E, Changsun Shin, Youngyun Cho, “Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city”, Building Research & Information, Vol. 49. no. 1, pp. 127-143, 2021.

[13]. Sathishkumar V E, Youngyun Cho, “A rule-based model for Seoul Bike sharing demand prediction using Weather data”, European Journal of Remote Sensing, Vol. 52, no. 1, pp. 166-183, 2020.

[14]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Seoul Bike Trip duration prediction using data mining techniques”, IET Intelligent Transport Systems, Vol. 14, no. 11, pp. 1465-1474, 2020.

[15]. Sathishkumar V E, Jangwoo Park, Youngyun Cho, “Using data mining techniques for bike sharing demand prediction in Metropolitan city”, Computer Communications, Vol. 153, pp. 353-366, 2020.

[16]. Sathishkumar V E, Yongyun Cho, “Season wise bike sharing demand analysis using random forest algorithm”, Computational Intelligence, pp. 1-26, 2020.

[17]. Sathishkumar, V. E., Wesam Atef Hatamleh, Abeer Ali Alnuaim, Mohamed Abdelhady, B. Venkatesh, and S. Santhoshkumar. "Secure Dynamic Group Data Sharing in Semi-trusted Third Party Cloud Environment." Arabian Journal for Science and Engineering (2021): 1-9.

[18]. Chen, J., Shi, W., Wang, X., Pandian, S., & Sathishkumar, V. E. (2021). Workforce optimisation for improving customer experience in urban transportation using heuristic mathematical model. International Journal of Shipping and Transport Logistics, 13(5), 538-553.

[19]. Pavithra, E., Janakiramaiah, B., Narasimha Prasad, L. V., Deepa, D., Jayapandian, N., & Sathishkumar, V. E., Visiting Indian Hospitals Before, During and After Covid. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 30 (1), 111-123, 2022.

[20]. Easwaramoorthy, S., Moorthy, U., Kumar, C. A., Bhushan, S. B., & Sadagopan, V. (2017, January). Content based image retrieval with enhanced privacy in cloud using apache spark. In International Conference on Data Science Analytics and Applications (pp. 114-128). Springer, Singapore.

[21]. Sathishkumar, V. E., Agrawal, P., Park, J., & Cho, Y. (2020, April). Bike Sharing Demand Prediction Using Multiheaded Convolution Neural Networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 264-265). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[22]. Subramanian, M., Shanmuga Vadivel, K., Hatamleh, W. A., Alnuaim, A. A., Abdelhady, M., & VE, S. (2021). The role of contemporary digital tools and technologies in Covid‐19 crisis: An exploratory analysis. Expert systems.

[23]. Babu, J. C., Kumar, M. S., Jayagopal, P., Sathishkumar, V. E., Rajendran, S., Kumar, S., ... & Mahseena, A. M. (2022). IoT-Based Intelligent System for Internal Crack Detection in Building Blocks. Journal of Nanomaterials, 2022.

[24]. Subramanian, M., Kumar, M. S., Sathishkumar, V. E., Prabhu, J., Karthick, A., Ganesh, S. S., & Meem, M. A. (2022). Diagnosis of retinal diseases based on Bayesian optimization deep learning network using optical coherence tomography images. Computational Intelligence and Neuroscience, 2022.

[25]. Liu, Y., Sathishkumar, V. E., & Manickam, A. (2022). Augmented reality technology based on school physical education training. Computers and Electrical Engineering, 99, 107807.

[26]. Sathishkumar, V. E., Rahman, A. B. M., Park, J., Shin, C., & Cho, Y. (2020, April). Using machine learning algorithms for fruit disease classification. In Basic & clinical pharmacology & toxicology (Vol. 126, pp. 253-253). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[27]. Sathishkumar, V. E., Venkatesan, S., Park, J., Shin, C., Kim, Y., & Cho, Y. (2020, April). Nutrient water supply prediction for fruit production in greenhouse environment using artificial neural networks. In Basic & Clinical Pharmacology & Toxicology (Vol. 126, pp. 257-258). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[28]. Sathishkumar, V. E., & Cho, Y. (2019, December). Cardiovascular disease analysis and risk assessment using correlation based intelligent system. In Basic & clinical pharmacology & toxicology (Vol. 125, pp. 61-61). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY.

[29]. Kotha, S. K., Rani, M. S., Subedi, B., Chunduru, A., Karrothu, A., Neupane, B., & Sathishkumar, V. E. (2021). A comprehensive review on secure data sharing in cloud environment. Wireless Personal Communications, 1-28.

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