SeveralCapsule-Forensics sophisticated convolutional neural network (CNN)Convolutional Neural Networks (CNN) architectures have been devised that have achieved impressive results in various domains. One downside of this success is the advent of attacks using deepfakesDeepFake, a family of tools that enable anyone to use a personal computer to easily create fake videos of someone from a short video found online. Several detectors have been introduced to deal with such attacks. To achieve state-of-the-art performance, CNN-basedConvolutional Neural Networks (CNN) detectors have usually been upgraded by increasing their depth and/or their width, adding more internal connections, or fusing several features or predicted probabilities from multiple CNNsConvolutional Neural Networks (CNN). As a result, CNN-basedConvolutional Neural Networks (CNN) detectors have become bigger, consume more memory and computation power, and require more training data. Moreover, there is concern about their generalizability to deal with unseen manipulation methods. In this chapter, we argue that our forensic-oriented capsule networkCapsule network overcomes these limitations and is more suitable than conventional CNNsConvolutional Neural Networks (CNN) to detect deepfakesDeepFake. The superiority of our ``Capsule-Forensics’’ Capsule-Forensics network is due to the use of a pretrained feature extractor, statistical pooling layers, and a dynamic routing algorithm. This design enables the Capsule-ForensicsCapsule-Forensics network to outperform a CNNConvolutional Neural Networks (CNN) with a similar design and to be from 5 to 11 times smaller than a CNNConvolutional Neural Networks (CNN) with similar performance.