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.

Theoretical Foundations and Mathematical Breakdown

1. Hyperdimensional Computing (HDC) for Frame-Level Encoding

Hyperdimensional Computing (HDC) leverages high-dimensional vectors, called hypervectors, to encode frame-level information compactly yet with rich spatial details. This process captures both the micro-patterns in each frame and the subtle variances that may arise from manipulations.

Mathematical Foundations of HDC

Hyperdimensional representations make the model robust to noise and small perturbations, preserving essential details while discarding redundant data.

2. Temporal Capsule Networks for Sequence Dependency Analysis

Temporal Capsule Networks capture relationships between video frames, identifying temporal inconsistencies such as irregular blinking, facial structure…