What Else Can Fool Deep Learning?
Addressing Color Constancy Errors on Deep Neural Network Performance

Mahmoud Afifi1 and Michael S. Brown1,2

1York University, 2Samsung Research



There is active research targeting local image manipulations that can fool deep neural networks (DNNs) into producing incorrect results. This paper examines a type of global image manipulation that can produce similar adverse effects. Specifically, we explore how strong color casts caused by incorrectly applied computational color constancy – referred to as white balance (WB) in photography – negatively impact the performance of DNNs targeting image segmentation and classification. In addition, we discuss how existing image augmentation methods used to improve the robustness of DNNs are not well suited for modeling WB errors. To address this problem, a novel augmentation method is proposed that can emulate accurate color constancy degradation. We also explore pre-processing training and testing images with a recent WB correction algorithm to reduce the effects of incorrectly white-balanced images. We examine both augmentation and pre-processing strategies on different datasets and demonstrate notable improvements on the CIFAR-10, CIFAR-100, and ADE20K datasets.

Effects of WB Errors on Pre-trained DNNs

We examine how errors related to computational color constancy can adversely affect DNNs focused on image classification and semantic segmentation. In addition, we show that image augmentation strategies used to expand the variation of training images are not well suited to mimic the type of image degradation caused by color constancy errors.

While incorrect color constancy is not an explicit attempt at an adversarial attack, the types of failures produced by this global modification act much like an untargeted attack and can adversely affect DNNs' performance.

Training with WB augmentation

We introduce a novel augmentation method that can accurately emulate realistic color constancy degradation. We also examine a newly proposed WB correction method to pre-process testing and training images. Experiments on CIFAR-10, CIFAR-100, and the ADE20K datasets using the proposed augmentation and pre-processing correction demonstrate notable improvements to test image inputs with color constancy errors.

Figure Files
Paper Supplementary Materials Poster Source Code
Paper Supplementary Materials Poster Code and Dataset

booktitle = {International Conference on Computer Vision (ICCV)},
title = {What Else Can Fool Deep Learning? Addressing Color Constancy Errors on Deep Neural Network Performance},
author = {Afifi, Mahmoud and Brown, Michael S.},
year = {2019},
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