Bias Field Poses a Threat to DNN-based X-Ray Recognition
arXiv:2009.09247
Abstract
The chest X-ray plays a key role in screening and diagnosis of many lung diseases including the COVID-19. More recently, many works construct deep neural networks (DNNs) for chest X-ray images to realize automated and efficient diagnosis of lung diseases. However, bias field caused by the improper medical image acquisition process widely exists in the chest X-ray images while the robustness of DNNs to the bias field is rarely explored, which definitely poses a threat to the X-ray-based automated diagnosis system. In this paper, we study this problem based on the recent adversarial attack and propose a brand new attack, i.e., the adversarial bias field attack where the bias field instead of the additive noise works as the adversarial perturbations for fooling the DNNs. This novel attack posts a key problem: how to locally tune the bias field to realize high attack success rate while maintaining its spatial smoothness to guarantee high realisticity. These two goals contradict each other and thus has made the attack significantly challenging. To overcome this challenge, we propose the adversarial-smooth bias field attack that can locally tune the bias field with joint smooth & adversarial constraints. As a result, the adversarial X-ray images can not only fool the DNNs effectively but also retain very high level of realisticity. We validate our method on real chest X-ray datasets with powerful DNNs, e.g., ResNet50, DenseNet121, and MobileNet, and show different properties to the state-of-the-art attacks in both image realisticity and attack transferability. Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.
6 pages, 5 figures; This work has been accepted to ICME 2021 as the oral presentation
References in corpus (9)
- CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
- Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
- Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
- Generative Poisoning Attack Method Against Neural Networks
- Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
- COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
- Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack
- Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection
- Defending against adversarial attacks on medical imaging AI system, classification or detection?