Advanced Deepfake Detection with Hyperdimensional Computing, Neuro-Symbolic Logic, Temporal Capsules and Memory-Augmented Networks
Introduction
As deepfake technology evolves, the demand for reliable and explainable deepfake detection systems has never been higher. Traditional deep learning models face limitations when dealing with the subtle artifacts and temporal inconsistencies that define deepfakes. To address these challenges, this article introduces an advanced hybrid neural network architecture I created that leverages Hyperdimensional Computing (HDC), Neuro-Symbolic Logic, Temporal Capsule Networks and Memory-Augmented Networks.
In this article, we’ll break down the mathematics and implementation of each component then present a complete code walkthrough, explore the architecture’s suitability for deepfake detection and analyse performance using cross-validation. Each component is optimized to process the unique temporal, spatial, and logical inconsistencies typical of deepfakes, making this setup particularly powerful in the fight against manipulated media.
