Integrating Symbolic and Neural Mechanisms for Adversarially Robust Hyperdimensional Computing

Abstract

Neuro-symbolic models may improve robustness by combining learned representations with structured composition, but their behavior under adversarial perturbation remains underexplored. We study a hybrid pipeline that fuses ViT features with classical descriptors through Hyperdimensional Computing (HDC). Across CIFAR10 (Subset), COIL100, and ETH80 under FGSM and Genetic Attack, ViT + Classical HDC degrades more gracefully than Pure HDC and ViT + HDC baselines. It achieves higher normalized AURC, lower attack-time decision margins, and larger gains from partial adversarial retraining. These results suggest that classical-neural fusion within HDC is a promising direction for robustness under the evaluated threat models.