Aug 2026· International Symposium on Low Power Electronics and Design· pp. 1-7· 0 citations· 22 references
Computer Science
TL;DR
Across CIFAR10, COIL100, and ETH80 under FGSM and Genetic Attack, ViT + Classical HDC degrades more gracefully than Pure HDC and ViT + HDC baselines, and achieves higher normalized AURC, lower attack-time decision margins, and larger gains from partial adversarial retraining.
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.
VisionDES is introduced, a novel DES framework for image classification that uses deep model embeddings to estimate classifier competence and consistently outperforms static ensembles and existing uncertainty-based DES methods, improving robust accuracy under strong attacks and under distribution shifts.
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