# FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images

Source: [FFT-based Selection and Optimization of Statistics for Robust Recognition of Severely Corrupted Images](<https://www.sota2.com/research/paper/68fa7bfb1e209131cea8b9a0>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:53:16.434Z

Authors: Elena Camuffo, Umberto Michieli, Jijoong Moon, Daehyun Kim, Mete Ozay

Published: 2024-03-21T12:01:54.000Z

## Abstract

Improving model robustness in case of corrupted images is among the key challenges to enable robust vision systems on smart devices, such as robotic agents. Particularly, robust test-time performance is imperative for most of the applications. This paper presents a novel approach to improve robustness of any classification model, especially on severely corrupted images. Our method (FROST) employs high-frequency features to detect input image corruption type, and select layer-wise feature normalization statistics. FROST provides the state-of-the-art results for different models and datasets, outperforming competitors on ImageNet-C by up to 37.1% relative gain, improving baseline of 40.9% mCE on severe corruptions.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2403.14335>)

[PDF](<https://arxiv.org/pdf/2403.14335.pdf>)