Explainable Deepfake Video Detection using Convolutional Neural Network and CapsuleNet

Deepfakes are becoming harder to detect with the naked eye: The paper notes that recent technological advancements have made deepfakes "nearly indistinguishable from reality," and that accessibility has increased to the point where even novices can create convincing versions. This underscores why automated detection tools are increasingly necessary.

AI can be used to fight AI-generated misinformation: The researchers propose using machine learning models (specifically CNN and CapsuleNet with LSTM) to automatically detect deepfake videos by analyzing individual frames. This demonstrates that responsible AI development includes building safeguards against misuse of AI technology itself.

Explainability is critical for public trust in detection systems: The paper emphasizes using "Explainable AI" to clarify how their detection model makes decisions, aiming to foster "transparent human-AI relationships." This reflects a responsible AI principle: detection systems shouldn't be black boxes—people need to understand why a video is flagged as fake.

The deepfake problem requires technical solutions because the problem is technical: Since deepfakes are created using sophisticated machine learning algorithms (GANs), the paper suggests that combating them requires equally sophisticated AI tools rather than relying solely on human judgment or media literacy.