A Lightweight Algorithm for Detecting Fake Multimedia Contents on Social Media
Abstract
The significant growth of the fourth industrial revolution (Industry 4.0) coupled with the widespread adoption of social media across the world has initiated new challenges that deserve the attention of researchers and industry leaders especially in detecting and preventing fake multimedia contents on social media. The forging of multimedia contents like videos and images for malicious activities is gradually becoming very rampant and this has serious psychological, health, political and economic consequences on the targeted individuals or close associates of the victims. The application of deepfake algorithms to make manipulated videos and images has contributed in making it very difficult to identify fake videos and images from the real multimedia contents. The availability of the internet and social media has made the spread of deepfake videos and images very fast and at an alarming rate. This research work understanding the dire need to detect deepfake videos and images (multimedia contents) proposes a lightweight algorithm to detect deepfake videos and images on social media platforms. The need for a lightweight algorithm is essential to enable low computational devices to be able to apply the algorithm without computational challenges and overheads. The proposed model has demonstrated a significant reduction in the computational and time complexities. The research work also presented a comparative analysis of some selected deep learning models with emphasis on the datasets used, their features and challenges identified.
References
Rafique, R., Gantassi, R., Amin, R., et al. (2023). Deep fake detection and classification using error-level analysis and deep learning. Scientific Reports, 13, 7422. https://doi.org/10.1038/s41598-023-34629-3
Mitra, A., Mohanty, S. P., Corcoran, P., et al. (2021). A machine learning based approach for deepfake detection in social media through key video frame extraction. SN Computer Science, 2(98). https://doi.org/10.1007/s42979-021-00495-x
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and improving the image quality of StyleGAN. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8107-8116).
Chou, H. T. G., & Edge, N. (2012). They are happier and having better lives than I am: The impact of using Facebook on perceptions of others’ lives. Cyberpsychology, Behavior, and Social Networking, 15(2), 117-121.
Li, Y., Chang, M., & Lyu, S. (2018). In ictu oculi: Exposing AI generated fake face videos by detecting eye blinking. arXiv preprint: arXiv:1806.02877.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Advances in Neural Information Processing Systems (pp. 2672-2680).
Denton, E. L., Chintala, S., Fergus, R., et al. (2015). Deep generative image models using a Laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems (pp. 1486-1494).
Radford, A., Metz, L., & Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint: arXiv:1511.06434.
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. arXiv preprint: arXiv:1703.06352.
Taigman, Y., Polyak, A., & Wolf, L. (2016). Unsupervised cross-domain image generation. arXiv preprint: arXiv:1611.02200.
Shrivastava, A., Pfister, T., Tuzel, O., Susskind, J., Wang, W., & Webb, R. (2017). Learning from simulated and unsupervised images through adversarial training. In CVPR (Vol. 3, No. 4, p. 6).
Liu, M.-Y., Breuel, T., & Kautz, J. (2017). Unsupervised image-to-image translation networks. In NIPS (pp. 700-708).
Garrido, P., Valgaerts, L., Rehmsen, O., Thormaehlen, T., Perez, P., & Theobalt, C. (2014). Automatic face reenactment. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 4217-4224).
Suwajanakorn, S., Seitz, S. M., & Kemelmacher-Shlizerman, I. (2017). Synthesizing Obama: Learning lip sync from audio. ACM Transactions on Graphics (TOG), 36(4).
Chesney, R., & Citron, D. (2018). Deepfakes: A looming crisis for national security, democracy, and privacy? [Blog post]. Lawfare Blog. Retrieved from https://www.lawfareblog.com/deepfakes-looming-crisis-national-security-democracy-and-privacy
Mitra, A., Mohanty, S. P., Corcoran, P., & Kougianos, E. (2020). A novel machine learning-based method for deepfake video detection in social media. In Proceedings of the 6th IEEE International Symposium on Smart Electronic Systems (iSES) (In Press).
Güera, D., & Delp, E. J. (2018). Deepfake video detection using recurrent neural networks. In Proceedings of the 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (pp. 1-6).
Afchar, D., Nozick, V., Yamagishi, J., & Echizen, I. (2018). MesoNet: A compact facial video forgery detection network. In Proceedings of IEEE International Workshop on Information Forensics and Security (WIFS) (pp. 1-7).
Nguyen, H. H., Yamagishi, J., & Echizen, I. (2019). Capsule-Forensics: Using capsule networks to detect forged images and videos. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2307-2311).
Hashmi, M. F., Ashish, B. K. K., Keskar, A. G., Bokde, N. D., Yoon, J. H., & Geem, Z. W. (2020). An exploratory analysis on visual counterfeits using conv-lstm hybrid architecture. IEEE Access, 8, 101293-101308.
Kumar, A., Bhavsar, A., & Verma, R. (2020). Detecting deepfakes with metric learning. In 2020 8th International Workshop on Biometrics and Forensics (IWBF) (pp. 1-6). IEEE.
Tayyab, M., Marjani, M., Jhanjhi, N. Z., Hashem, I. A. T., Usmani, R. S. A., & Qamar, F. (2023). A comprehensive review on deep learning algorithms: Security and privacy issues. Computers & Security, 131, 103297.
Zuo, C., Qian, J., Feng, S., et al. (2022). Deep learning in optical metrology: a review. Light: Science & Applications, 11(1), 39. https://doi.org/10.1038/s41377-022-00714-x
Rosenblatt, F. (1958). The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408.
LeCun, Y., et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation, 1(4), 541-551.
Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527-1554.
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems (pp. 1097-1105).
Xu, K., et al. (2018). How powerful are graph neural networks? In Proceedings of the 7th International Conference on Learning Representations (ICLR).
Zhou, J., et al. (2020). Graph neural networks: A review of methods and applications. AI Open, 1, 57-81.
Yang, X., Zhang, Z., Zhu, W., et al. (2020). Deterministic conversion rule for CNNs to efficient spiking convolutional neural networks. Science China Information Sciences, 63(12), 122402. https://doi.org/10.1007/s11432-019-1468-0
Sarker, I. H. (2021). Deep learning: A comprehensive overview on techniques, taxonomy, applications, and research directions. SN Computer Science, 2, 420. https://doi.org/10.1007/s42979-021-00815-1
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15(1), 1929-1958
Schoenherr, J. R. (2023, April 12). The disturbing trend of state media use of deepfakes. The Conversation. https://theconversation.com/the-disturbing-trend-of-state-media-use-of-deepfakes-203368
Miller, J. R. (2022, March 17). Deepfake video of Zelensky telling Ukrainians to surrender removed from social platforms. New York Post. https://nypost.com/2022/03/17/deepfake-video-shows-volodymyr-zelensky-telling-ukrainians-to-surrender/
Shahid, F., Kamath, S., Sidotam, A., Jiang, V., Batino, A., & Vashistha, A. (2022). “It Matches My Worldview”: Examining Perceptions and Attitudes Around Fake Videos. In CHI Conference on Human Factors in Computing Systems (CHI ‘22) (pp. 1-15). ACM. https://doi.org/10.1145/3491102.3517646
Vota, W. (2022, August 25). Deepfake videos deeply fool Indian social media users. ICTworks. https://www.ictworks.org/deepfake-videos-deeply-fool-indian-social-media-users/
Satariano, A., & Mozur, P. (2023, February 7). The people onscreen are fake. The disinformation is real. The New York Times. https://www.nytimes.com/2023/02/07/technology/artificial-intelligence-training-deepfake.html
Hasan, H. R., & Salah, K. (2019). Combating deepfake videos using blockchain and smart contracts. IEEE Access, 7, 41596-41606.
Vedaldi, A., & Lenc, K. (2015). MatConvNet - Convolutional Neural Networks for MATLAB. arXiv preprint: arXiv:1412.4564. https://doi.org/10.48550/arXiv.1412.4564
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