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Recent Advancements in the Field of Deepfake Detection


Affiliations
1 Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin, United States
2 Department of Computer Information Science Minnesota State University, Mankato, Mankato, MN, United States
 

A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.

Keywords

Deepfake, Detection, Neural network, Dataset
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  • N. Siddiqui, R. Dave, M. Vanamala, and N. Seliya, "Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication," Machine Learning and Knowledge Extraction, vol. 4, no. 2, pp. 502-518, 2022. Available: https://doi.org/10.3390/make4020023.

  • L. Pryor, R. Dave, and M. Vanamala, "Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM," arXiv preprint arXiv:2302.10280, 2023. Available: https://arxiv.org/abs/2302.10280.

  • Zhou, Y., & Lim, S. N. (2021). “Joint audio-visual deepfake detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 14800-14809).

  • P. Kawa, M. Plata, and P. Syga, "SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection," in 2022 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Wuhan, China, 2022 pp. 792-799. doi: 10.1109/TrustCom56396.2022.00111.

  • Cozzolino, D., Rössler, A., Thies, J., Nießner, M., & Verdoliva, L. (2021). “Id-reveal: Identityaware deepfake video detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15108-15117).

  • L. Pryor, J. Mallet, R. Dave, N. Seliya, M. Vanamala, and E. S. Boone, "Evaluation of a User Authentication Schema Using Behavioral Biometrics and Machine Learning," arXiv preprint arXiv:2205.08371, 2022. Available: https://arxiv.org/abs/2205.08371.

  • A. Tiwari, R. Dave, and M. Vanamala, "Leveraging Deep Learning Approaches for Deepfake Detection: A Review," arXiv preprint arXiv:2304.01908, 2023. Available: https://arxiv.org/abs/2304.01908.

  • X. Dong, J. Bao, D. Chen, W. Zhang, N. Yu, D. Chen, F. Wen, and B. Guo, "Identity-Driven DeepFake Detection," 2020.

  • S. Kokal, M. Vanamala, and R. Dave, "Deep Learning and Machine Learning, Better Together Than Apart: A Review on Biometrics Mobile Authentication," Journal of Cybersecurity and Privacy, vol. 3, no. 2, pp. 227-258, 2023. Available: https://doi.org/10.3390/jcp3020013.

  • Güera, D., & Delp, E. J. (2018, November). “Deepfake video detection using recurrent neural networks,” in 2018 15th IEEE international conference on advanced video and signal based surveillance (AVSS) (pp. 1-6). IEEE.

  • Liu, J., Zhu, K., Lu, W., Luo, X., & Zhao, X. (2021). “A lightweight 3D convolutional neural network for deepfake detection.” International Journal of Intelligent Systems, 36(9), 4990-5004.

  • D. Wodajo and S. Atnafu, "Deepfake video detection using convolutional vision transformer," arXiv preprint arXiv:2102.11126, Feb. 2021.

  • A. Khormali and J. S. Yuan, "ADD: attention-based deepfake detection approach," Big Data and Cognitive Computing, vol. 5, no. 4, pp. 49, 2021.

  • T. Wang and K. P. Chow, "A Lightweight Reliably Quantified Deepfake Detection Approach," in ADFSL Conference on Digital Forensics, Security and Law, 2022.

  • [11] H. Wu, J. Zhou, S. Zhang, and J. Tian, "Exploring Spatial-Temporal Features for Deepfake Detection and Localization," in Proceedings of the IEEE International Conference on Multimedia and Expo (ICME), 2022.

  • Š. Hubálovský, P. Trojovský, N. Bacanin, and K. V., "Evaluation of deepfake detection using YOLO with local binary pattern histogram," PeerJ Computer Science, vol. 8, p. e1086, 2022. Available: https://doi.org/10.7717/peerj-cs.1086.

  • S. Das, S. Seferbekov, A. Datta, M. S. Islam, and M. R. Amin, "Towards solving the deepfake problem: An analysis on improving deepfake detection using dynamic face augmentation," in Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 3776-3785, 2021.

  • B. Hosler et al., "Do Deepfakes Feel Emotions? A Semantic Approach to Detecting Deepfakes Via Emotional Inconsistencies," 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Nashville, TN, USA, 2021, pp. 1013-1022, doi: 10.1109/CVPRW53098.2021.00112.

  • J. Mallet, N. Krueger, M. Vanamala, and R. Dave, "Hybrid Deepfake Detection Utilizing MLP and LSTM," arXiv preprint arXiv:2304.14504, 2023. Available: https://arxiv.org/abs/2304.14504.

  • T. Jung, S. Kim and K. Kim, "DeepVision: Deepfakes Detection Using Human Eye Blinking Pattern," in IEEE Access, vol. 8, pp. 83144-83154, 2020, doi: 10.1109/ACCESS.2020.2988660.

  • J. Mallet, R. Dave, N. Seliya, and M. Vanamala, "Using Deep Learning to Detecting Deepfakes," arXiv preprint arXiv:2207.13644, 2022. Available: https://arxiv.org/abs/2207.13644.

  • W. Z. Tee, R. Dave, N. Seliya, and M. Vanamala, "Human Activity Recognition models using Limited Consumer Device Sensors and Machine Learning," in 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML), Hangzhou, China, 2022, pp. 456-461, doi: 10.1109/CACML55074.2022.00083.

  • W. Z. Tee, R. Dave, J. Seliya and M. Vanamala, "A Close Look into Human Activity Recognition Models using Deep Learning," 2022 3rd International Conference on Computing, Networks and Internet of Things (CNIOT), Qingdao, China, 2022, pp. 201-206, doi: 10.1109/CNIOT55862.2022.00043.

  • L. Guarnera, O. Giudice and S. Battiato, "DeepFake Detection by Analyzing Convolutional Traces," in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Seattle, WA, USA, 2020 pp. 2841-2850.

  • Y. Li and S. Lyu, "Exposing deepfake videos by detecting face warping artifacts." arXiv preprint arXiv:1811.00656.

  • Y. A. Chuming, D. J. Wu, and K. Hong, "Practical Deepfake Detection: Vulnerabilities in Global Contexts." arXiv preprint arXiv:2206.09842.

  • S. A. Fezza, M. Y. Ouis, B. Kaddar, W. Hamidouche, and A. Hadid, "Evaluation of Pre-Trained CNN Models for Geographic Fake Image Detection," in 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), September 2022, pp. 1-6.

  • A. Jevnisek and S. Avidan, "Aggregating Layers for Deepfake Detection," in 2022 26th International Conference on Pattern Recognition (ICPR), Montreal, QC, Canada, 2022, pp. 2027- 2033, doi: 10.1109/ICPR56361.2022.9956052.

  • M. Groh, Z. Epstein, C. Firestone, and R. Picard, "Deepfake detection by human crowds, machines, and machine-informed crowds," Proceedings of the National Academy of Sciences, vol. 119, no. 1, pp. e2110013119, 2022.

  • P. Korshunov and S. Marcel, "Deepfake detection: humans vs. machines," arXiv preprint arXiv:2009.03155.

  • N. M. Müller, K. Pizzi, and J. Williams, "Human perception of audio deepfakes," in Proceedings of the 1st International Workshop on Deepfake Detection for Audio Multimedia, October 2022, pp. 85- 91.

  • D. Siegel, C. Kraetzer, S. Seidlitz, and J. Dittmann, “Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features,” Journal of Imaging, vol. 7, no. 7, p. 108, Jul. 2021, doi: 10.3390/jimaging7070108.

  • A. V. Nadimpalli and A. Rattani, "GBDF: gender balanced deepfake dataset towards fair deepfake detection," arXiv preprint arXiv:2207.10246.

  • L. Trinh and Y. Liu, "An examination of fairness of AI models for deepfake detection," arXiv preprint arXiv:2105.00558.

  • K. Narayan, H. Agarwal, K. Thakral, S. Mittal, M. Vatsa and R. Singh, "DeePhy: On Deepfake Phylogeny," in 2022 IEEE International Joint Conference on Biometrics (IJCB), Abu Dhabi, United Arab Emirates, 2022, pp. 1-10, doi: 10.1109/IJCB54206.2022.10007968.

  • Z. DeRidder et al., "Continuous User Authentication Using Machine Learning and Multi-finger Mobile Touch Dynamics with a Novel Dataset," in 2022 9th International Conference on Soft Computing & Machine Intelligence (ISCMI), Toronto, ON, Canada, 2022, pp. 42-46, doi: 10.1109/ISCMI56532.2022.10068450.


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  • Recent Advancements in the Field of Deepfake Detection

Abstract Views: 54  |  PDF Views: 36

Authors

Natalie Krueger
Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin, United States
Mounika Vanamala
Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, Wisconsin, United States
Rushit Dave
Department of Computer Information Science Minnesota State University, Mankato, Mankato, MN, United States

Abstract


A deepfake is a photo or video of a person whose image has been digitally altered or partially replaced with an image of someone else. Deepfakes have the potential to cause a variety of problems and are often used maliciously. A common usage is altering videos of prominent political figures and celebrities. These deepfakes can portray them making offensive, problematic, and/or untrue statements. Current deepfakes can be very realistic, and when used in this way, can spread panic and even influence elections and political opinions. There are many deepfake detection strategies currently in use but finding the most comprehensive and universal method is critical. So, in this survey we will address the problems of malicious deepfake creation and the lack of universal deepfake detection methods. Our objective is to survey and analyze a variety of current methods and advances in the field of deepfake detection.

Keywords


Deepfake, Detection, Neural network, Dataset

References