ArXiv

Certified Training for Convolutional Perturbations

2026-07-20 · 17:36 UTC ·Benedikt Brückner, Alessio Lomuscio ·1 min read
Authors
Benedikt Brückner, Alessio Lomuscio
Categories
cs.CV, cs.LG
arXiv
https://arxiv.org/abs/2607.18195v1
PDF
https://arxiv.org/pdf/2607.18195v1

Brief

Certified Training for Convolutional Perturbations (Brückner & Lomuscio, 2026) proposes an efficient encoding of convolutional perturbations to train models with formal robustness guarantees against runtime effects like camera-induced motion blur. The approach yields over 80% robust accuracy on CIFAR-10 for realistic blur levels, reportedly outperforming Adversarial Training while keeping standard accuracy comparable, addressing safety gaps in empirical defenses.

Why it matters

Introduces a Certified Training method that encodes convolutional perturbations (e.g., motion blur) to train models with provable robustness guarantees; reports over 80% robust accuracy on CIFAR-10 against motion blur of reasonable intensity.

Key details

  • Method reportedly significantly outperforms Adversarial Training while maintaining comparable standard accuracy; authors Benedikt Brückner and Alessio Lomuscio published the arXiv preprint (2607.18195v1) on 2026-07-20 in cs.CV and cs.LG.
Source evidence

Abstract

Vision models have been found to be susceptible to perturbations such as motion blur induced at runtime by a shaking camera. This impedes their deployment in critical applications since phenomena such as slightly blurred vision might lead to failures, for example an object detector missing objects. While methods such as data augmentation or Adversarial Training can improve empirical robustness, they lack formal safety guarantees, making it difficult to identify and mitigate hidden vulnerabilities. We introduce a novel Certified Training approach that leverages an efficient encoding of convolutional perturbations to train provably robust models. Our method significantly outperforms Adversarial Training, achieving, for example, over 80% robust accuracy against motion blur of reasonable intensity on CIFAR10 while maintaining comparable standard accuracy.