Diffusion-Based Feature Denoising with NNMF for Robust handwritten digit multi-class classification

TL;DR

This paper introduces a robust multi-class handwritten digit classification method combining diffusion-driven feature denoising with hybrid feature representations, enhancing noise and adversarial attack robustness.

Contribution

It extends previous two-class frameworks to multi-class digit classification using diffusion-based denoising and hybrid features from NNMF and CNNs.

Findings

The diffusion-based hybrid model improves robustness against adversarial attacks.

CNN baseline models outperform traditional methods while maintaining high accuracy.

The method effectively denoises features and enhances classification reliability.

Abstract

This work presents a robust multi-class classification framework for handwritten digits that combines diffusion-driven feature denoising with a hybrid feature representation. Inspired by our previous work on brain tumor classification, the proposed approach operates in a feature space to improve the robustness to noise and adversarial attacks. This manuscript is submitted as an extended abstract rather than a full-length press-ready paper. First, the input images are converted into tight, interpretable exemplification using Non-negative Matrix Factorization (NNMF). In parallel, special deep features are extracted using a computational neural network (CNN). These integral features are combined into a united hybrid representation. The main objective of this work is to extend our previously validated two-class framework to a multi-class handwritten digit classification scenario. To improve robustness, a step diffusion operation is used in the feature space by gradually adding Gaussian noise. A feature denoiser network is trained to reverse this operation and rebuild clean representations from tilted inputs. The courteous features are then applied for multi-class classification. The suggested method is evaluated in both baseline and adversarial settings using AutoAttack. The experimental outcome present that the diffusion-based hybrid model is both effective and robust, the CNN baseline models outperforming while maintain powerful classification performance. These results explain the activity of feature-level diffusion defense for reliable multi-class handwritten digit classification.