Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

Cheng, Guohua; Ji, Hongli

doi:10.1109/access.2020.3030235

Cited by 16 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 4 and Table 5 provides a basic comparison of the vulnerability of various systems employing various adversarial attacks. For instance, in the domain of healthcare application, Cheng et al (Cheng & Ji, 2020) exploit the vulnerability of the CNN model which performs tumor detection using brain MRIs. They have also employed the universal adversarial perturbations to create adversarial MRIs to fool CNN.…”

Section: Discussionmentioning

confidence: 99%

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Bukhari

Yasmin

Gillani

et al. 2023

Journal of Database Management

View full text Add to dashboard Cite

Currently, the internet of everything (IoE) enabled smart surveillance systems are widely used in various fields to prevent various forms of abnormal behaviors. The authors assess the vulnerability of surveillance systems based on human gait and suggest a defense strategy to secure them. Human gait recognition is a promising biometric technology, but one significantly hindered because of universal adversarial perturbation (UAP) that may trigger system failure. More specifically, in this research study, the authors emphasize on sample convolutional neural network (CNN) model design for gait recognition and assess its susceptibility to UAPs. The authors compute the perturbation as non-targeted UAPs, which trigger a model failure and lead to an inaccurate label to the input sample of a given subject. The findings show that a smart surveillance system based on human gait analysis is susceptible to UAPs, even if the norm of the generated noise is substantially less than the average norm of the images. Later, in the next stage, the authors illustrate a defense mechanism to design a secure surveillance system based on human gait.

show abstract

Section: Discussionmentioning

confidence: 99%

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Bukhari

Yasmin

Gillani

et al. 2023

Journal of Database Management

View full text Add to dashboard Cite

show abstract

“…The decline in image quality observed in these instances frequently arises from the presence of irregular illumination. In light of this concern, Cheng et al [63] addressed the matter by using an adversarial attack technique. The researchers presented a new type of attack known as the "adversarial exposure attack".…”

Section: A Medical Image Adversarial Attack and Defense Classificatio...mentioning

confidence: 99%

A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

Muoka,

Yi,

Ukwuoma

et al. 2023

Mathematics

View full text Add to dashboard Cite

Deep learning approaches have demonstrated great achievements in the field of computer-aided medical image analysis, improving the precision of diagnosis across a range of medical disorders. These developments have not, however, been immune to the appearance of adversarial attacks, creating the possibility of incorrect diagnosis with substantial clinical implications. Concurrently, the field has seen notable advancements in defending against such targeted adversary intrusions in deep medical diagnostic systems. In the context of medical image analysis, this article provides a comprehensive survey of current advancements in adversarial attacks and their accompanying defensive strategies. In addition, a comprehensive conceptual analysis is presented, including several adversarial attacks and defensive strategies designed for the interpretation of medical images. This survey, which draws on qualitative and quantitative findings, concludes with a thorough discussion of the problems with adversarial attack and defensive mechanisms that are unique to medical image analysis systems, opening up new directions for future research. We identified that the main problems with adversarial attack and defense in medical imaging include dataset and labeling, computational resources, robustness against target attacks, evaluation of transferability and adaptability, interpretability and explainability, real-time detection and response, and adversarial attacks in multi-modal fusion. The area of medical imaging adversarial attack and defensive mechanisms might move toward more secure, dependable, and therapeutically useful deep learning systems by filling in these research gaps and following these future objectives.

show abstract

“…MRI images for brain tumor segmentation provide four different modalities (T1, T2, T1ce, and FLAIR) with different intensities in order for the brain tumor to be detected and labeled easily. Cheng et al [69] investigated the effects of adversarial examples when they are applied on each modality and in all modalities simultaneously. Experiments were carried out with an ensemble U-Net model and MICCAI BraTS 2019 [70] dataset.…”

Section: Existing Adversarial Attacks On Medical Imagesmentioning

confidence: 99%

A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

Apostolidis

Papakostas

2021

Electronics

View full text Add to dashboard Cite

In the past years, deep neural networks (DNN) have become popular in many disciplines such as computer vision (CV), natural language processing (NLP), etc. The evolution of hardware has helped researchers to develop many powerful Deep Learning (DL) models to face numerous challenging problems. One of the most important challenges in the CV area is Medical Image Analysis in which DL models process medical images—such as magnetic resonance imaging (MRI), X-ray, computed tomography (CT), etc.—using convolutional neural networks (CNN) for diagnosis or detection of several diseases. The proper function of these models can significantly upgrade the health systems. However, recent studies have shown that CNN models are vulnerable under adversarial attacks with imperceptible perturbations. In this paper, we summarize existing methods for adversarial attacks, detections and defenses on medical imaging. Finally, we show that many attacks, which are undetectable by the human eye, can degrade the performance of the models, significantly. Nevertheless, some effective defense and attack detection methods keep the models safe to an extent. We end with a discussion on the current state-of-the-art and future challenges.

show abstract

Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

Cited by 16 publications

References 24 publications

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

Secure Gait Recognition-Based Smart Surveillance Systems Against Universal Adversarial Attacks

A Comprehensive Review and Analysis of Deep Learning-Based Medical Image Adversarial Attack and Defense

A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis

Contact Info

Product

Resources

About