| Title | DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Rana, M. S., Sung, A. H. |
| Conference Name | 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom) |
| Keywords | Computer crime, counterfeit contents, Deep Ensemble Learning, deep ensemble learning technique, deep ensemble-based, deep learning models, DeepFake, deepfake detection, Deepfake multimedia, DeepfakeStack, gan, GANs, generative adversarial network models, Greedy Layer-wise Pretraining, Human Behavior, human factors, human perception, hyper-realistic images, improved composite classifier, innovative applications, learning (artificial intelligence), legitimate applications, manipulated audio, manipulated videos, Metrics, numerous nefarious ways, pattern classification, Political Campaigns, pubcrawl, Realtime Deepfake detector, resilience, Resiliency, Scalability, state-of-art classification models, unlawful ways, video clips |
| Abstract | Recent advances in technology have made the deep learning (DL) models available for use in a wide variety of novel applications; for example, generative adversarial network (GAN) models are capable of producing hyper-realistic images, speech, and even videos, such as the so-called "Deepfake" produced by GANs with manipulated audio and/or video clips, which are so realistic as to be indistinguishable from the real ones in human perception. Aside from innovative and legitimate applications, there are numerous nefarious or unlawful ways to use such counterfeit contents in propaganda, political campaigns, cybercrimes, extortion, etc. To meet the challenges posed by Deepfake multimedia, we propose a deep ensemble learning technique called DeepfakeStack for detecting such manipulated videos. The proposed technique combines a series of DL based state-of-art classification models and creates an improved composite classifier. Based on our experiments, it is shown that DeepfakeStack outperforms other classifiers by achieving an accuracy of 99.65% and AUROC of 1.0 score in detecting Deepfake. Therefore, our method provides a solid basis for building a Realtime Deepfake detector. |
| DOI | 10.1109/CSCloud-EdgeCom49738.2020.00021 |
| Citation Key | rana_deepfakestack_2020 |
DeepfakeStack: A Deep Ensemble-based Learning Technique for Deepfake Detection