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Uncovering the Strength of Capsule Networks in Deepfake Detection

Information is everywhere, and sometimes we have no idea if what we read, watch or listen is accurate, real or authentic. This paper focuses on detecting deep learning generated videos, or deepfakes - a phenomenon which is more and more present in today's society. While there are some very good methods of detecting deepfakes, there are two key elements that should always be considered, i.e., no method is perfect and deepfake generation techniques continue to evolve, sometimes even faster than detection methods. In our proposed architectures, we focus on a family of deep learning methods that is new, has several advantages over traditional Convolutional Neural Networks and has been underutilized in the fight against fake information, namely the Capsule Networks. We show that: (i) state-of-the-art Capsule Network architectures can be improved in the context of deepfake detection, (ii) they can be used to obtain accurate results using a very small number of parameters, and (iii) Capsule Networks are a viable option over deep convolutional models. Experimental validation is carried out on two publicly available datasets, namely FaceForensics++ and CelebDF, showing very promising results.