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The Security of Deep Learning Defences for Medical Imaging [article]

Moshe Levy, Guy Amit, Yuval Elovici, Yisroel Mirsky
2022 arXiv   pre-print
Deep learning has shown great promise in the domain of medical image analysis. Medical professionals and healthcare providers have been adopting the technology to speed up and enhance their work.  ...  We show that an informed attacker can evade five of the current state of the art defences while successfully fooling the victim's deep learning model, rendering these defences useless.  ...  Acknowledgements This material is based upon work supported by the Zuckerman STEM Leadership Program.  ... 
arXiv:2201.08661v1 fatcat:5mjayrp2czeuplymcwvc2gy3oi

Conference Speaker

2021 2021 6th International Conference on Communication, Image and Signal Processing (CCISP)  
In this talk, intelligent medical image processing and analysis with deep learning are introduced, including 1) virtual medical imaging for separation of bones from soft tissue in chest x-ray images, 2  ...  My group has been actively studying on deep learning in medical imaging in the past 25 years.  ...  My research interests are the broad area of computer vision, machine learning, and deep learning.  ... 
doi:10.1109/ccisp52774.2021.9639085 fatcat:a3kelf54vrcvtidjgpxyqsj2sa

Model Fooling Attacks Against Medical Imaging: A Short Survey

Tuomo Sipola, Samir Puuska, Tero Kokkonen
2020 Information & Security An International Journal  
Acknowledgements This research is partially funded by the Cyber Security Network of Competence Centres for Europe (CyberSec4Europe) project of the Horizon 2020 SU-ICT-03-2018 program.  ...  See 3 for a survey of adversarial attacks against deep learning in computer vision. The authors not only list several attacks but also include defences.  ...  As a powerful machine learning method, deep learning has also been applied to images related to pathology, for example, trying to classify images of cancer whole slide images (WSI).  ... 
doi:10.11610/isij.4615 fatcat:vg5xo6wiwfgk5pnm66d2bfgi5u

Blockchain based Attack Detection on Machine Learning Algorithms for IoT based E-Health Applications [article]

Thippa Reddy Gadekallu, Manoj M K, Sivarama Krishnan S, Neeraj Kumar, Saqib Hakak, Sweta Bhattacharya
2020 arXiv   pre-print
The application of machine learning (ML) algorithms are massively scaling-up due to rapid digitization and emergence of new tecnologies like Internet of Things (IoT).  ...  Hence, in this article, we have proposed blockchain based solution to secure the datasets generated from IoT devices for E-Health applications.  ...  as a counter-measure, Foveated Imaging Mechanism, Randomization of Data 2) Adversarial Attack for network model: Deep Contractive Network, Regularization and Masking of the Gradient, Defensive Filtration  ... 
arXiv:2011.01457v1 fatcat:lnjypiwznfbfhhzl2ttcfwtgsi

Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps [article]

Yujin Huang, Han Hu, Chunyang Chen
2021 arXiv   pre-print
Apart from the feasibility of the model attack, we also carry out an empirical study that investigates the characteristics of deep learning models used by hundreds of Android apps on Google Play.  ...  Deep learning has shown its power in many applications, including object detection in images, natural-language understanding, and speech recognition.  ...  Despite numerous studies that have proposed various adversarial attack methods and corresponding defence strategies for deep learning models, work on the security of deep learning models on mobile apps  ... 
arXiv:2101.04401v2 fatcat:hknqkmp6njehrifzfbxo5mvm4u

Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks [article]

Nima Mirnateghi, Syed Afaq Ali Shah, Mohammed Bennamoun
2021 arXiv   pre-print
In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in the physical world applications.  ...  Deep learning has become an integral part of various computer vision systems in recent years due to its outstanding achievements for object recognition, facial recognition, and scene understanding.  ...  ACKNOWLEDGMENTS This research has been supported by Murdoch Univeristy, and is partially supported by the Australian Research Council (Grants DP150100294 and DP150104251).  ... 
arXiv:2108.10217v1 fatcat:s6hmgbf2sffj3c3rldm6dbefou

A Systematic Review on Machine Learning and Deep Learning Models for Electronic Information Security in Mobile Networks

Chaitanya Gupta, Ishita Johri, Kathiravan Srinivasan, Yuh-Chung Hu, Saeed Mian Qaisar, Kuo-Yi Huang
2022 Sensors  
We address the necessity to develop new approaches to provide high security of electronic data in mobile networks because the possibilities for increasing mobile network security are inexhaustible.  ...  According to the research, an artificial intelligence-based security model should assure the secrecy, integrity, and authenticity of the system, its equipment, and the protocols that control the network  ...  Nomenclature of current deep learning models for electronic information security. Figure 5 . 5 Figure 5. Nomenclature of current deep learning models for electronic information security.  ... 
doi:10.3390/s22052017 pmid:35271163 pmcid:PMC8915055 fatcat:6khxq7pkyzgifdos7ifcyqsmgi

Deep Image Restoration Model: A Defense Method Against Adversarial Attacks

Kazim Ali, Adnan N. Quershi, Ahmad Alauddin Bin Arifin, Muhammad Shahid Bhatti, Abid Sohail, Rohail Hassan
2022 Computers Materials & Continua  
These days, deep learning and computer vision are much-growing fields in this modern world of information technology.  ...  We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence.  ...  Deep learning (DL) is the subfield of Machine Learning (ML), and ML is the subfield of Artificial Intelligence (AI).  ... 
doi:10.32604/cmc.2022.020111 fatcat:yd5ocnn73zbovb2inytp2zvase

Table of contents

2020 2020 14th International Conference on Innovations in Information Technology (IIT)  
Security and Privacy Applications Framework for Protecting the Confidentiality of Outsourced Data on CloudTasneemAli Ghunaim (UAE -DUBAI & University Of Sharjah, United Arab Emirates), Ibrahim Kamel (University  ...  (Alexandria University, Egypt) 120 Self-supervised Deep Learning for Flower Image Segmentation Sudipan Saha (Fondazione Bruno Kessler, Italy), Nasrullah Sheikh (IBM, USA), Biplab Banerjee (Indian Institute  ... 
doi:10.1109/iit50501.2020.9299089 fatcat:dpormcuvbrdnnig5ei6mf42v2u

An integrated Auto Encoder-Block Switching defense approach to prevent adversarial attacks [article]

Anirudh Yadav, Ashutosh Upadhyay, S.Sharanya
2022 arXiv   pre-print
According to recent studies, the vulnerability of state-of-the-art Neural Networks to adversarial input samples has increased drastically.  ...  The attack is planned using FGSM [9] model, and the subsequent counter-attack by the proposed architecture will take place thereby demonstrating the feasibility and security delivered by the algorithm.  ...  INTRODUCTION Machine Learning (ML) has spread its wings in almost all domains from medical to industrial equipment maintenance [1] .  ... 
arXiv:2203.10930v1 fatcat:fawcfut6sjezjoaop6ywzsbpvu

Kryptonite: An Adversarial Attack Using Regional Focus [chapter]

Yogesh Kulkarni, Krisha Bhambani
2021 Lecture Notes in Computer Science  
Over the past few years, applications of Deep Learning using Deep Neural Networks(DNN) in several fields including Medical Diagnosis, Security Systems, Virtual Assistants, etc. have become extremely commonplace  ...  In this paper, we present a novel study analyzing the weaknesses in the security of deep learning systems. We propose 'Kryptonite', an adversarial attack on images.  ...  Medical datasets were hence chosen for experimentation of the attack for two reasons. The first reason for doing so is to demonstrate threats on a real life application of deep learning.  ... 
doi:10.1007/978-3-030-81645-2_26 fatcat:5yp533stwbghhhvw7l2grub6iy

Backdoor Attack is a Devil in Federated GAN-based Medical Image Synthesis [article]

Ruinan Jin, Xiaoxiao Li
2022 arXiv   pre-print
Deep Learning-based image synthesis techniques have been applied in healthcare research for generating medical images to support open research.  ...  We show that combining the two defense strategies yields a robust medical image generation.  ...  Acknowledgement This work is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and NVIDIA Hardware Award.  ... 
arXiv:2207.00762v2 fatcat:vl3c273alvhhfonybepazy3ivu

A Survey of Adversarial Machine Learning in Cyber Warfare

Vasisht Duddu
2018 Defence Science Journal  
We explore the threat models for Machine Learning systems and describe the various techniques to attack and defend them.  ...  Adversarial machine learning is a fast growing area of research which studies the design of Machine Learning algorithms that are robust in adversarial environments.  ...  Medical and health-care domains for instance, using ML need to ensure privacy and data leakage prevention.  ... 
doi:10.14429/dsj.68.12371 fatcat:vyupcxe6hrhllb4rowequxrf5i

Table of Contents

2021 2021 International Conference on Communication information and Computing Technology (ICCICT)  
Jadhav 64 Context-Based Deep Learning Approach for Named Entity Recognition in Hindi Sarika Singh, Shashank Patel, Rucha Nargunde, Yash Shah, Jyoti Ramteke 65 Securing folder directory using image encryption  ...  Sangeeta Joshi 121 Deep Learning Model for Pothole Detection and Area Computation Surekha Arjapure, D.R.Kalbande 122 Image Captioning based Smart Navigation System for Visually Impaired Chinmayi Rane,  ... 
doi:10.1109/iccict50803.2021.9510086 fatcat:rt3zoqcytnhv3im2zb3mhhue3m

Distinguishing Lightweight Block Ciphers in Encrypted Images

Girish Mishra, S. K. Pal, S. V. S. S. N. V. G. Krishna Murthy, Kanishk Vats, Rakshak Raina
2021 Defence Science Journal  
We make use of images from MNIST and fashion MNIST data sets for establishing the cryptographic distinguisher.  ...  We try to establish a deep learning based method to identify the encryption scheme used from a set of three lightweight block ciphers viz. LBlock, PRESENT and SPECK.  ...  DEEP LEARNING APPROACH FOR EXPERIMENTS Deep Learning, the latest discipline in machine learning techniques consisting of various learning methods, are primarily based on artificial Neural Network (aNN)  ... 
doi:10.14429/dsj.71.16843 fatcat:n62ggzxmijdqne4aqglkhvecrq
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