Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features

Alex Liew; Sos Agaian; Liang Zhao

doi:10.1117/12.2664239

15 June 2023 Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features

Alex Liew, Sos Agaian, Liang Zhao

Author Affiliations +

Proceedings Volume 12526, Multimodal Image Exploitation and Learning 2023 ; 125260O (2023) https://doi.org/10.1117/12.2664239
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Conference Poster

Abstract

Skin cancer is the most common type of cancer in United States with 9,500 new cases diagnosed daily. It is one of the deadliest forms, however early detection and treatments can lead to recovery. More and more modern medical systems employs deep learning (DL) vision models as an assistive secondary diagnostic tool. This progress is derived from the superior performance by convolutional neural networks (CNNs) across a wide number of medical applications. However, recent discovery has revealed that adding small but faint noises to images can cause these models to make classification errors. These adversarial attacks can undermine defense measures and hamper the operations of deep learning models in real-world settings. The objective of this paper is to explore the effects of image degradation on popular off-the-shelf Deep Learning (DL) vision models. First, the investigation of the effects of adversarial attacks on image classification accuracy, sensitivity, and specificity are evaluated. Then we introduce pepper noise as an adversarial attack, which is an extension of the one-pixel attack on deep learning models. Second, we propose a novel texture descriptor Ordered statistics Local Binary Patterns (OS-LBP) for recognizing potential skin cancer areas. Third, we will demonstrate how our OS-LBP is successful in mitigating some of the effects of image degradations caused by adversarial attacks.

Citation Download Citation

Alex Liew, Sos Agaian, and Liang Zhao "Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features", Proc. SPIE 12526, Multimodal Image Exploitation and Learning 2023 , 125260O (15 June 2023); https://doi.org/10.1117/12.2664239

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE