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

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Outcomes of Adversarial Attacks on Deep Learning Models for Ophthalmology Imaging Domains
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Enhancing adversarial defense for medical image analysis systems with pruning and attention mechanism
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Some Investigations on Robustness of Deep Learning in Limited Angle Tomography
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Generalizability vs. Robustness: Investigating Medical Imaging Networks Using Adversarial Examples
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Vulnerability Analysis of Chest X-Ray Image Classification Against Adversarial Attacks
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Improving Robustness of Medical Image Diagnosis with Denoising Convolutional Neural Networks
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Impact of Adversarial Examples on Deep Learning Models for Biomedical Image Segmentation
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Brain MR Image Segmentation in Small Dataset with Adversarial Defense and Task Reorganization
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Intelligent Image Synthesis to Attack a Segmentation CNN Using Adversarial Learning
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Influence of Control Parameters and the Size of Biomedical Image Datasets on the Success of Adversarial Attacks
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Bridging the Gap Between AI and Healthcare Sides: Towards Developing Clinically Relevant AI-Powered Diagnosis Systems
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Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search
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Robustification of Segmentation Models Against Adversarial Perturbations in Medical Imaging
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Realistic Adversarial Data Augmentation for MR Image Segmentation
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Defending Deep Learning-Based Biomedical Image Segmentation from Adversarial Attacks: A Low-Cost Frequency Refinement Approach
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Miss the Point: Targeted Adversarial Attack on Multiple Landmark Detection
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Towards Robust General Medical Image Segmentation
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Adversarial Attacks Medical Diagnosis Model with Generative Adversarial Networks
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Addressing Adversarial Machine Learning Attacks in Smart Healthcare Perspectives
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Detecting One-Pixel Attacks Using Variational Autoencoders
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How Effective is Adversarial Training of CNNs in Medical Image Analysis?
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Analysis of the Impact of White Box Adversarial Attacks in ResNet While Classifying Retinal Fundus Images
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Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification
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MIRST-DM: Multi-instance RST with Drop-Max Layer for Robust Classification of Breast Cancer
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Adversarially Robust Prototypical Few-Shot Segmentation with Neural-ODEs
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An Efficient Defending Mechanism Against Image Attacking on Medical Image Segmentation Models
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Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations
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Skin Disease Recognition Using Deep Saliency Features and Multimodal Learning of Dermoscopy and Clinical Images
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Feature-Guided Black-Box Safety Testing of Deep Neural Networks
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Vulnerability Analysis and Robust Training with Additive Noise for FGSM Attack on Transfer Learning-Based Brain Tumor Detection from MRI
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Robustifying Deep Networks for Medical Image Segmentation
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Increasing-Margin Adversarial (IMA) training to improve adversarial robustness of neural networks
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LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images
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Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images
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Frequency constraint-based adversarial attack on deep neural networks for medical image classification
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Improving adversarial robustness of medical imaging systems via adding global attention noise
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Reversing skin cancer adversarial examples by multiscale diffusive and denoising aggregation mechanism
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Defense strategies for Adversarial Machine Learning: A survey
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Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection
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Adversarial Attacks and Defenses in Deep Learning
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Collaborative Defense-GAN for protecting adversarial attacks on classification system
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Query-based black-box attack against medical image segmentation model
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Defending against adversarial attacks on Covid-19 classifier: A denoiser-based approach
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Digitization of healthcare sector: A study on privacy and security concerns
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Target attack on biomedical image segmentation model based on multi-scale gradients
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A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images
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Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images
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Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
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Towards evaluating the robustness of deep diagnostic models by adversarial attack
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Adversarial attack vulnerability of medical image analysis systems: Unexplored factors
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Enhancing MR image segmentation with realistic adversarial data augmentation
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Anatomical context protects deep learning from adversarial perturbations in medical imaging
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Understanding adversarial attacks on deep learning based medical image analysis systems
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Robust convolutional neural networks against adversarial attacks on medical images
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Defense Against Adversarial Attacks with Efficient Frequency-Adaptive Compression and Reconstruction
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Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
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A deep-learning estimate of the decadal trends in the Southern Ocean carbon storage
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Adversarial attacks and adversarial robustness in computational pathology
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Intuitive physics learning in a deep-learning model inspired by developmental psychology
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Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach
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Intellectual Tool to Compute Embodied Energy and Carbon Dioxide Emission for Building Construction Materials
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A Novel Automating Irrigation Techniques based on Artificial Neural Network and Fuzzy Logic
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Classification of Diabetic Retinopathy Using Adversarial Training
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Adversarial attacks and adversarial robustness in computational pathology
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Deep Learning Applications in Medical Image Analysis
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Can AI Help in Screening Viral and COVID-19 Pneumonia?
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Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation
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NSL-MHA-CNN: A Novel CNN Architecture for Robust Diabetic Retinopathy Prediction Against Adversarial Attacks
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Adversarial Attacks and Defenses in Machine Learning-Empowered Communication Systems and Networks: A Contemporary Survey
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DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
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Deep Residual Learning for Image Recognition
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ChestX-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
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Robust Physical-World Attacks on Deep Learning Visual Classification
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Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser
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A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations
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The Limitations of Deep Learning in Adversarial Settings
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Adversarial Examples for Semantic Segmentation and Object Detection
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SafetyNet: Detecting and Rejecting Adversarial Examples Robustly
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Analysis of the Effect of Black Box Adversarial Attacks on Medical Image Classification Models
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Analysis of the No-sign Adversarial Attack on the COVID Chest X-ray Classification
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Predictive COVID-19 Risk and Virus Mutation isolation with CNN based Machine learning Technique
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Impact of Adversarial Training on the Robustness of Deep Neural Networks
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Bias Field Poses a Threat to DNN-Based X-Ray Recognition
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Analysis of Adversarial Attacks on Skin Cancer Recognition
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Adversarial Attacks and Defences for Skin Cancer Classification
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Attack-agnostic Adversarial Detection on Medical Data Using Explainable Machine Learning
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Evaluating Robustness Against Adversarial Attacks: A Representational Similarity Analysis Approach
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Multi-Modal Adversarial Example Detection with Transformer
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Susceptibility to misdiagnosis of adversarial images by deep learning based retinal image analysis algorithms
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Self-Supervision vs. Transfer Learning: Robust Biomedical Image Analysis Against Adversarial Attacks
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Robust Detection of Adversarial Attacks on Medical Images
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Mitigating Adversarial Attacks on Medical Image Understanding Systems
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Defending Against Adversarial Attacks On Medical Imaging Ai System, Classification Or Detection?
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Adversarial attacks on deep learning models for fatty liver disease classification by modification of ultrasound image reconstruction method
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Adversarial Exposure Attack on Diabetic Retinopathy Imagery Grading
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Adversarial Examples—Security Threats to COVID-19 Deep Learning Systems in Medical IoT Devices
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Medical Image Analysis
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Towards Evaluating the Robustness of Neural Networks
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A Feature Space-Restricted Attention Attack on Medical Deep Learning Systems
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One Pixel Attack for Fooling Deep Neural Networks
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Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
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Attention Residual Learning for Skin Lesion Classification
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Multi-Modal Deep Guided Filtering for Comprehensible Medical Image Processing
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No Surprises: Training Robust Lung Nodule Detection for Low-Dose CT Scans by Augmenting With Adversarial Attacks
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MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack
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Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
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Image Segmentation Using Deep Learning: A Survey
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Adversarial robustness study of convolutional neural network for lumbar disk shape reconstruction from MR images
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Adversarial attacks on medical machine learning
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Biomedical Image Recognition in Pulmonology and Oncology with the Use of Deep Learning
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Defense Against Adversarial Attacks Based on Stochastic Descent Sign Activation Networks on Medical Images
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Practical Black-Box Attacks against Machine Learning
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AdvU-Net: Generating Adversarial Example Based on Medical Image and Targeting U-Net Model
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Universal adversarial attacks on deep neural networks for medical image classification
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On the role of deep learning model complexity in adversarial robustness for medical images
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Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
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Optimization of Incremental Sheet Forming Process Using Artificial Intelligence-Based Techniques
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Handbook of Approximation Algorithms and Metaheuristics
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Classification of Complex Diseases using an Improved Binary Cuckoo Search and Conditional Mutual Information Maximization
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ADVERSARIAL ATTACK DRIVEN DATA AUGMENTATION FOR ACCURATE AND ROBUST MEDICAL IMAGE SEGMENTATION
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Using deep transfer learning to detect scoliosis and spondylolisthesis from x-ray images
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Non-Local Context Encoder: Robust Biomedical Image Segmentation against Adversarial Attacks
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Adversarial training for prostate cancer classification using magnetic resonance imaging
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Impact of Adversarial Examples on the Efficiency of Interpretation and Use of Information from High-Tech Medical Images
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Failure Detection in Deep Neural Networks for Medical Imaging
inferredcites →https://doi.org/10.3390/app11094233openalex-snapshot
Vulnerability in Deep Transfer Learning Models to Adversarial Fast Gradient Sign Attack for COVID-19 Prediction from Chest Radiography Images
inferredcites →https://doi.org/10.3390/bioengineering10020194openalex-snapshot
The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System
inferredcites →https://doi.org/10.3390/bioengineering9110709openalex-snapshot
Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images
inferredcites →https://doi.org/10.3390/biomedicines10102545openalex-snapshot
How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)
inferredcites →https://doi.org/10.3390/cancers15051548openalex-snapshot
Comparing Detection Schemes for Adversarial Images against Deep Learning Models for Cancer Imaging
inferredcites →https://doi.org/10.3390/diagnostics12051152openalex-snapshot
Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head
inferredcites →https://doi.org/10.3390/diagnostics13081416openalex-snapshot
Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images
inferredcites →https://doi.org/10.3390/jcm11082265openalex-snapshot
Clinical Applications of Artificial Intelligence—An Updated Overview
inferredcites →https://doi.org/10.3390/jcm12093266openalex-snapshot
Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology
inferredcites →https://doi.org/10.3390/jimaging8020038openalex-snapshot
Natural Images Allow Universal Adversarial Attacks on Medical Image Classification Using Deep Neural Networks with Transfer Learning
inferredcites →https://doi.org/10.3390/jimaging8060155openalex-snapshot
Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning
inferredcites →https://doi.org/10.3390/s21113922openalex-snapshot
Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition
inferredcites →https://doi.org/10.3390/s23146378openalex-snapshot
Fooling Examples: Another Intriguing Property of Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot
Towards Deep Learning Models Resistant to Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.1801.02613openalex-snapshot
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
inferredcites →https://doi.org/10.48550/arxiv.1804.08598openalex-snapshot
Black-box Adversarial Attacks with Limited Queries and Information
inferredcites →https://doi.org/10.48550/arxiv.1902.03368openalex-snapshot
Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
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Practical Fast Gradient Sign Attack against Mammographic Image Classifier
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Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
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A Thorough Comparison Study on Adversarial Attacks and Defenses for Common Thorax Disease Classification in Chest X-rays
inferredcites →https://doi.org/10.48550/arxiv.2104.00139openalex-snapshot
Adversarial Heart Attack: Neural Networks Fooled to Segment Heart Symbols in Chest X-Ray Images
inferredcites →https://doi.org/10.48550/arxiv.2106.01544openalex-snapshot
SSMD: Semi-Supervised Medical Image Detection with Adaptive Consistency and Heterogeneous Perturbation
inferredcites →https://doi.org/10.48550/arxiv.2111.10969openalex-snapshot
Medical Aegis: Robust adversarial protectors for medical images
inferredcites →https://doi.org/10.48550/arxiv.2206.01736openalex-snapshot
Adaptive Adversarial Training to Improve Adversarial Robustness of DNNs for Medical Image Segmentation and Detection
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Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey
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FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE
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Adversarial Attacks on Image Classification Models: FGSM and Patch Attacks and Their Impact
inferredcites →https://doi.org/10.61185/smij.2022.2315openalex-snapshot
Building a Sustainable Social Feedback Loop: A Machine Intelligence Approach for Twitter Opinion Mining
inferredcites →https://doi.org/10.61185/smij.2022.8463openalex-snapshot
DeepHAR-Net: A Novel Machine Intelligence Approach for Human Activity Recognition from Inertial Sensors
inferredcites →https://doi.org/10.61185/smij.hpao9103openalex-snapshot
A Machine Learning Solution for Securing the Internet of Things Infrastructures

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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