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Adversarial Attacks and Countermeasures on Image Classification-based Deep Learning Models in Autonomous Driving Systems: A Systematic Review

https://doi.org/10.1145/3691625
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inferredcites →https://doi.org/10.1007/978-3-030-00470-5_13openalex-snapshot
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
inferredcites →https://doi.org/10.1007/978-3-030-58555-6_17openalex-snapshot
Boosting Decision-Based Black-Box Adversarial Attacks with Random Sign Flip
inferredcites →https://doi.org/10.1007/978-3-030-58558-7_36openalex-snapshot
Indirect Local Attacks for Context-Aware Semantic Segmentation Networks
inferredcites →https://doi.org/10.1007/978-3-030-58592-1_29openalex-snapshot
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search
inferredcites →https://doi.org/10.1007/978-3-030-60248-2_27openalex-snapshot
Adversarial Attacks on Deep Learning Models of Computer Vision: A Survey
inferredcites →https://doi.org/10.1007/978-3-031-20065-6_10openalex-snapshot
Triangle Attack: A Query-Efficient Decision-Based Adversarial Attack
inferredcites →https://doi.org/10.1007/978-3-031-30648-8_7openalex-snapshot
Boosting Adversarial Training in Adversarial Machine Learning
inferredcites →https://doi.org/10.1007/978-981-19-7222-5_12openalex-snapshot
Deceiving Traffic Sign Recognition with Physical One-Pixel Attacks
inferredcites →https://doi.org/10.1007/978-981-19-7943-9_21openalex-snapshot
Backdoor Attack Against Deep Learning-Based Autonomous Driving with Fogging
inferredcites →https://doi.org/10.1007/978-981-97-4182-3_26openalex-snapshot
Towards Score-Based Black-Box Adversarial Examples Attack in Real World
inferredcites →https://doi.org/10.1007/s00371-022-02660-6openalex-snapshot
A survey on adversarial attacks and defenses for object detection and their applications in autonomous vehicles
inferredcites →https://doi.org/10.1007/s00521-020-04761-6openalex-snapshot
An optimal pruning algorithm of classifier ensembles: dynamic programming approach
inferredcites →https://doi.org/10.1007/s10462-021-10123-yopenalex-snapshot
Visual privacy attacks and defenses in deep learning: a survey
inferredcites →https://doi.org/10.1007/s10586-021-03421-1openalex-snapshot
Evaluation of adversarial machine learning tools for securing AI systems
inferredcites →https://doi.org/10.1007/s10664-021-10064-8openalex-snapshot
Omni: automated ensemble with unexpected models against adversarial evasion attack
inferredcites →https://doi.org/10.1007/s11042-018-5853-4openalex-snapshot
Adversarial image detection in deep neural networks
inferredcites →https://doi.org/10.1007/s11042-020-10139-6openalex-snapshot
A review on genetic algorithm: past, present, and future
inferredcites →https://doi.org/10.1007/s11416-023-00486-xopenalex-snapshot
Potential cyber threats of adversarial attacks on autonomous driving models
inferredcites →https://doi.org/10.1007/s11633-019-1211-xopenalex-snapshot
Adversarial Attacks and Defenses in Images, Graphs and Text: A Review
inferredcites →https://doi.org/10.1007/s42154-023-00220-9openalex-snapshot
An Adversarial Attack on Salient Regions of Traffic Sign
inferredcites →https://doi.org/10.1016/c2020-0-01078-9openalex-snapshot
Adversarial Robustness for Machine Learning
inferredcites →https://doi.org/10.1016/j.asoc.2024.111729openalex-snapshot
Feature-aware transferable adversarial attacks against image classification
inferredcites →https://doi.org/10.1016/j.compeleceng.2022.108446openalex-snapshot
Improving adversarial transferability by temporal and spatial momentum in urban speaker recognition systems
inferredcites →https://doi.org/10.1016/j.cose.2021.102269openalex-snapshot
A survey on security attacks and defense techniques for connected and autonomous vehicles
inferredcites →https://doi.org/10.1016/j.eswa.2018.07.026openalex-snapshot
Fine-tuning Convolutional Neural Networks for fine art classification
inferredcites →https://doi.org/10.1016/j.ifacol.2016.11.078openalex-snapshot
Use of IoT Technology to Drive the Automotive Industry from Connected to Full Autonomous Vehicles
inferredcites →https://doi.org/10.1016/j.inffus.2021.11.005openalex-snapshot
A comprehensive survey on regularization strategies in machine learning
inferredcites →https://doi.org/10.1016/j.jnca.2020.102632openalex-snapshot
Adversarial attacks on deep-learning-based SAR image target recognition
inferredcites →https://doi.org/10.1016/j.patcog.2023.109382openalex-snapshot
Defense Against Adversarial Attacks with Efficient Frequency-Adaptive Compression and Reconstruction
inferredcites →https://doi.org/10.1016/j.patcog.2023.109430openalex-snapshot
Quantifying the preferential direction of the model gradient in adversarial training with projected gradient descent
inferredcites →https://doi.org/10.1016/j.patcog.2023.110035openalex-snapshot
Traffic sign attack via pinpoint region probability estimation network
inferredcites →https://doi.org/10.1016/j.patrec.2024.01.010openalex-snapshot
Time-aware and task-transferable adversarial attack for perception of autonomous vehicles
inferredcites →https://doi.org/10.1016/j.tra.2020.03.024openalex-snapshot
Automated drivability: Toward an assessment of the spatial deployment of level 4 automated vehicles
inferredcites →https://doi.org/10.1039/9781847556561-00251openalex-snapshot
Engineering and technology
inferredcites →https://doi.org/10.1109/access.2020.3048120openalex-snapshot
Gradient Masking of Label Smoothing in Adversarial Robustness
inferredcites →https://doi.org/10.1109/access.2023.3337638openalex-snapshot
Data Poisoning Attacks With Hybrid Particle Swarm Optimization Algorithms Against Federated Learning in Connected and Autonomous Vehicles
inferredcites →https://doi.org/10.1109/access.2024.3407097openalex-snapshot
D-BADGE: Decision-Based Adversarial Batch Attack With Directional Gradient Estimation
inferredcites →https://doi.org/10.1109/comst.2019.2963791openalex-snapshot
Toward Proactive, Adaptive Defense: A Survey on Moving Target Defense
inferredcites →https://doi.org/10.1109/cvpr.1993openalex-snapshot
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.17openalex-snapshot
Universal Adversarial Perturbations
inferredcites →https://doi.org/10.1109/cvpr.2018.00175openalex-snapshot
Robust Physical-World Attacks on Deep Learning Visual Classification
inferredcites →https://doi.org/10.1109/cvpr.2018.00191openalex-snapshot
Defense Against Adversarial Attacks Using High-Level Representation Guided Denoiser
inferredcites →https://doi.org/10.1109/cvpr.2018.00467openalex-snapshot
Geometric Robustness of Deep Networks: Analysis and Improvement
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/cvpr.2019.00095openalex-snapshot
Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples
inferredcites →https://doi.org/10.1109/cvpr.2019.00444openalex-snapshot
Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks
inferredcites →https://doi.org/10.1109/cvpr.2019.00669openalex-snapshot
Barrage of Random Transforms for Adversarially Robust Defense
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01426openalex-snapshot
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01446openalex-snapshot
Detecting Adversarial Samples Using Influence Functions and Nearest Neighbors
inferredcites →https://doi.org/10.1109/dicta60407.2023.00075openalex-snapshot
Deep Bayesian Image Set Classification Approach for Defense against Adversarial Attacks
inferredcites →https://doi.org/10.1109/dsn.2019.00068openalex-snapshot
Robust Anomaly Detection on Unreliable Data
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/focs52979.2021.00098openalex-snapshot
Feature Purification: How Adversarial Training Performs Robust Deep Learning
inferredcites →https://doi.org/10.1109/iavvc57316.2023.10328098openalex-snapshot
Data-Driven Defenses Against Adversarial Attacks for Autonomous Vehicles
inferredcites →https://doi.org/10.1109/icassp43922.2022.9747294openalex-snapshot
Wassertrain: An Adversarial Training Framework Against Wasserstein Adversarial Attacks
inferredcites →https://doi.org/10.1109/iccv.2019.00483openalex-snapshot
Enhancing Adversarial Example Transferability With an Intermediate Level Attack
inferredcites →https://doi.org/10.1109/icdm.2015.84openalex-snapshot
A Unified Gradient Regularization Family for Adversarial Examples
inferredcites →https://doi.org/10.1109/icdm58522.2023.00089openalex-snapshot
ATTA: Adversarial Task-transferable Attacks on Autonomous Driving Systems
inferredcites →https://doi.org/10.1109/icip40778.2020.9191288openalex-snapshot
Universal Adversarial Attack Via Enhanced Projected Gradient Descent
inferredcites →https://doi.org/10.1109/icip49359.2023.10223158openalex-snapshot
GNP Attack: Transferable Adversarial Examples Via Gradient Norm Penalty
inferredcites →https://doi.org/10.1109/icivc55077.2022.9886997openalex-snapshot
Adversarial Attacks and Defenses in Image Classification: A Practical Perspective
inferredcites →https://doi.org/10.1109/icme.2019.00095openalex-snapshot
Untargeted Adversarial Attack via Expanding the Semantic Gap
inferredcites →https://doi.org/10.1109/icse48619.2023.00188openalex-snapshot
An Empirical Study of Deep Learning Models for Vulnerability Detection
inferredcites →https://doi.org/10.1109/ijcnn.2019.8852078openalex-snapshot
Targeted Black-Box Adversarial Attack Method for Image Classification Models
inferredcites →https://doi.org/10.1109/iscc58397.2023.10218291openalex-snapshot
Driving into Danger: Adversarial Patch Attack on End-to-End Autonomous Driving Systems Using Deep Learning
inferredcites →https://doi.org/10.1109/ism59092.2023.00040openalex-snapshot
Towards Imperceptible Adversarial Image Generation: Minimizing Perceptual Difference
inferredcites →https://doi.org/10.1109/jas.2021.1004108openalex-snapshot
Generating Adversarial Samples on Multivariate Time Series using Variational Autoencoders
inferredcites →https://doi.org/10.1109/jiot.2015.2442956openalex-snapshot
Microlocation for Internet-of-Things-Equipped Smart Buildings
inferredcites →https://doi.org/10.1109/jiot.2020.3016145openalex-snapshot
Adaptive Square Attack: Fooling Autonomous Cars With Adversarial Traffic Signs
inferredcites →https://doi.org/10.1109/jiot.2020.3034899openalex-snapshot
Targeted Attention Attack on Deep Learning Models in Road Sign Recognition
inferredcites →https://doi.org/10.1109/jiot.2024.3405006openalex-snapshot
OptiCloak: Blinding Vision-Based Autonomous Driving Systems Through Adversarial Optical Projection
inferredcites →https://doi.org/10.1109/jproc.2019.2948775openalex-snapshot
The Security of Autonomous Driving: Threats, Defenses, and Future Directions
inferredcites →https://doi.org/10.1109/mci.2022.3155327openalex-snapshot
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
inferredcites →https://doi.org/10.1109/msp.2020.2975749openalex-snapshot
Federated Learning: Challenges, Methods, and Future Directions
inferredcites →https://doi.org/10.1109/msp.2020.2985363openalex-snapshot
Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective
inferredcites →https://doi.org/10.1109/niles50944.2020.9257874openalex-snapshot
A proposed IoT based Smart traffic lights control system within a V2X framework
inferredcites →https://doi.org/10.1109/ojits.2022.3142612openalex-snapshot
Countering Adversarial Attacks on Autonomous Vehicles Using Denoising Techniques: A Review
inferredcites →https://doi.org/10.1109/ojvt.2023.3265363openalex-snapshot
Cybersecurity of Autonomous Vehicles: A Systematic Literature Review of Adversarial Attacks and Defense Models
inferredcites →https://doi.org/10.1109/percom45495.2020.9127389openalex-snapshot
An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-snapshot
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/sp.2019.00031openalex-snapshot
Neural Cleanse: Identifying and Mitigating Backdoor Attacks in Neural Networks
inferredcites →https://doi.org/10.1109/spw.2018.00015openalex-snapshot
Learning Universal Adversarial Perturbations with Generative Models
inferredcites →https://doi.org/10.1109/tdsc.2020.3014390openalex-snapshot
Defending Against Adversarial Attack Towards Deep Neural Networks Via Collaborative Multi-Task Training
inferredcites →https://doi.org/10.1109/tdsc.2023.3289298openalex-snapshot
Decision-Based Query Efficient Adversarial Attack via Adaptive Boundary Learning
inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot
One Pixel Attack for Fooling Deep Neural Networks
inferredcites →https://doi.org/10.1109/tii.2021.3071405openalex-snapshot
Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses
inferredcites →https://doi.org/10.1109/tip.2022.3184255openalex-snapshot
Boosting Fast Adversarial Training With Learnable Adversarial Initialization
inferredcites →https://doi.org/10.1109/tits.2023.3347860openalex-snapshot
Fooling Decision-Based Black-Box Automotive Vision Perception Systems in Physical World
inferredcites →https://doi.org/10.1109/tnnls.2018.2886017openalex-snapshot
Adversarial Examples: Attacks and Defenses for Deep Learning
inferredcites →https://doi.org/10.1109/tpami.2022.3169802openalex-snapshot
Query-Efficient Black-Box Adversarial Attack With Customized Iteration and Sampling
inferredcites →https://doi.org/10.1109/tpami.2024.3392760openalex-snapshot
Towards Unified Robustness Against Both Backdoor and Adversarial Attacks
inferredcites →https://doi.org/10.1109/tvt.2021.3061065openalex-snapshot
Multi-Source Adversarial Sample Attack on Autonomous Vehicles
inferredcites →https://doi.org/10.1109/wacv.2018.00093openalex-snapshot
Towards Robust Deep Neural Networks with BANG
inferredcites →https://doi.org/10.1109/wacv51458.2022.00288openalex-snapshot
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks
inferredcites →https://doi.org/10.1109/wacv57701.2024.00387openalex-snapshot
Natural Light Can Also be Dangerous: Traffic Sign Misinterpretation Under Adversarial Natural Light Attacks
inferredcites →https://doi.org/10.1111/ijmr.12156openalex-snapshot
A Systematic Literature Review on Recent Lean Research: State‐of‐the‐art and Future Directions
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Efficient Schemes for Total Variation Minimization Under Constraints in Image Processing
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3128572.3140448openalex-snapshot
ZOO
inferredcites →https://doi.org/10.1145/3205455openalex-snapshot
Proceedings of the Genetic and Evolutionary Computation Conference
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Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
inferredcites →https://doi.org/10.1145/3321707.3321749openalex-snapshot
GenAttack
inferredcites →https://doi.org/10.1145/3330204.3330282openalex-snapshot
A Non-Deterministic Method to Construct Ensemble-Based Classifiers to Protect Decision Support Systems Against Adversarial Images
inferredcites →https://doi.org/10.1145/3377811.3380422openalex-snapshot
DeepBillboard
inferredcites →https://doi.org/10.1145/3485133openalex-snapshot
Adversarial Machine Learning in Image Classification: A Survey Toward the Defender’s Perspective
inferredcites →https://doi.org/10.1145/3485730.3485935openalex-snapshot
Adversarial Attacks against LiDAR Semantic Segmentation in Autonomous Driving
inferredcites →https://doi.org/10.1145/3548606.3560561openalex-snapshot
Post-breach Recovery
inferredcites →https://doi.org/10.1145/3560905openalex-snapshot
Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
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Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security
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Invisible Optical Adversarial Stripes on Traffic Sign against Autonomous Vehicles
inferredcites →https://doi.org/10.1145/3672359.3672362openalex-snapshot
Denoising Autoencoder-Based Defensive Distillation as an Adversarial Robustness Algorithm Against Data Poisoning Attacks
inferredcites →https://doi.org/10.1145/383259.383296openalex-snapshot
Image quilting for texture synthesis and transfer
inferredcites →https://doi.org/10.1155/2023/6376275openalex-snapshot
Multiround Transfer Learning and Modified Generative Adversarial Network for Lung Cancer Detection
inferredcites →https://doi.org/10.1201/9781003338611-6openalex-snapshot
IoT Technologies for Connected and Automated Driving Applications
inferredcites →https://doi.org/10.14722/ndss.2018.23198openalex-snapshot
Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks
inferredcites →https://doi.org/10.15607/rss.2024.xx.076openalex-snapshot
Dynamic Adversarial Attacks on Autonomous Driving Systems
inferredcites →https://doi.org/10.1609/aaai.v32i1.11302openalex-snapshot
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
inferredcites →https://doi.org/10.1609/aaai.v33i01.33011028openalex-snapshot
Perceptual-Sensitive GAN for Generating Adversarial Patches
inferredcites →https://doi.org/10.18178/ijmlc.2018.8.3.688openalex-snapshot
Gradient Masking Is a Type of Overfitting
inferredcites →https://doi.org/10.18653/v1/2020.acl-main.249openalex-snapshot
Weight Poisoning Attacks on Pretrained Models
inferredcites →https://doi.org/10.18653/v1/p19-1openalex-snapshot
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
inferredcites →https://doi.org/10.2139/ssrn.4772716openalex-snapshot
The Unseen Adversaries: Robust and Generalized Defense Against Adversarial Patches
inferredcites →https://doi.org/10.2478/acss-2021-0012openalex-snapshot
Adversarial Attacks and Defense Technologies on Autonomous Vehicles: A Review
inferredcites →https://doi.org/10.24963/ijcai.2018/543openalex-snapshot
Generating Adversarial Examples with Adversarial Networks
inferredcites →https://doi.org/10.3233/faia240141openalex-snapshot
Gradient-Based Adversarial Example Generation with Root Mean Square Propagation
inferredcites →https://doi.org/10.3233/faia385openalex-snapshot
Artificial Intelligence and Human-Computer Interaction
inferredcites →https://doi.org/10.3390/electronics11121814openalex-snapshot
Defending Adversarial Attacks against DNN Image Classification Models by a Noise-Fusion Method
inferredcites →https://doi.org/10.3390/electronics12061464openalex-snapshot
Boosting Adversarial Attacks with Nadam Optimizer
inferredcites →https://doi.org/10.3390/fi13030073openalex-snapshot
Deep Model Poisoning Attack on Federated Learning
inferredcites →https://doi.org/10.3390/sym10120738openalex-snapshot
Random Untargeted Adversarial Example on Deep Neural Network
inferredcites →https://doi.org/10.36227/techrxiv.19071824.v1openalex-snapshot
A Hybrid Defense Method against Adversarial Attacks on Traffic Sign Classifiers in Autonomous Vehicles
inferredcites →https://doi.org/10.36548/jucct.2022.4.002openalex-snapshot
Analysis of Smart Sign Board Design for Road Safety using IoT
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1603.05145openalex-snapshot
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions
inferredcites →https://doi.org/10.48550/arxiv.1605.07277openalex-snapshot
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1702.06763openalex-snapshot
DeepCloak: Masking Deep Neural Network Models for Robustness Against Adversarial Samples
inferredcites →https://doi.org/10.48550/arxiv.1703.04318openalex-snapshot
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems
inferredcites →https://doi.org/10.48550/arxiv.1705.03387openalex-snapshot
Generative Adversarial Trainer: Defense to Adversarial Perturbations with GAN
inferredcites →https://doi.org/10.48550/arxiv.1707.05373openalex-snapshot
Houdini: Fooling Deep Structured Prediction Models
inferredcites →https://doi.org/10.48550/arxiv.1711.00117openalex-snapshot
Countering Adversarial Images using Input Transformations
inferredcites →https://doi.org/10.48550/arxiv.1711.01991openalex-snapshot
Mitigating Adversarial Effects Through Randomization
inferredcites →https://doi.org/10.48550/arxiv.1804.06872openalex-snapshot
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
inferredcites →https://doi.org/10.48550/arxiv.1805.07894openalex-snapshot
Constructing Unrestricted Adversarial Examples with Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1805.10917openalex-snapshot
Deep Anomaly Detection Using Geometric Transformations
inferredcites →https://doi.org/10.48550/arxiv.1809.08352openalex-snapshot
Unrestricted Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1810.00069openalex-snapshot
Adversarial Attacks and Defences: A Survey
inferredcites →https://doi.org/10.48550/arxiv.1904.13000openalex-snapshot
Adversarial Training and Robustness for Multiple Perturbations
inferredcites →https://doi.org/10.48550/arxiv.1906.03563openalex-snapshot
Adversarial Attack Generation Empowered by Min-Max Optimization
inferredcites →https://doi.org/10.48550/arxiv.1906.06919openalex-snapshot
Improving Black-box Adversarial Attacks with a Transfer-based Prior
inferredcites →https://doi.org/10.48550/arxiv.1911.07140openalex-snapshot
Black-Box Adversarial Attack with Transferable Model-based Embedding
inferredcites →https://doi.org/10.48550/arxiv.1912.01206openalex-snapshot
Deep Probabilistic Models to Detect Data Poisoning Attacks
inferredcites →https://doi.org/10.48550/arxiv.2001.03994openalex-snapshot
Fast is better than free: Revisiting adversarial training
inferredcites →https://doi.org/10.48550/arxiv.2007.00753openalex-snapshot
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
inferredcites →https://doi.org/10.48550/arxiv.2007.02617openalex-snapshot
Understanding and Improving Fast Adversarial Training
inferredcites →https://doi.org/10.48550/arxiv.2101.11453openalex-snapshot
Meta Adversarial Training against Universal Patches
inferredcites →https://doi.org/10.48550/arxiv.2110.03445openalex-snapshot
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks
inferredcites →https://doi.org/10.48550/arxiv.2204.02844openalex-snapshot
Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training
inferredcites →https://doi.org/10.48550/arxiv.2211.01579openalex-snapshot
Data-free Defense of Black Box Models Against Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.2403.08170openalex-snapshot
Versatile Defense Against Adversarial Attacks on Image Recognition
inferredcites →https://doi.org/10.5220/0007714203070318openalex-snapshot
MultiMagNet: A Non-deterministic Approach based on the Formation of Ensembles for Defending Against Adversarial Images
inferredcites →https://doi.org/10.5220/0012314700003636openalex-snapshot
GenGradAttack: Efficient and Robust Targeted Adversarial Attacks Using Genetic Algorithms and Gradient-Based Fine-Tuning

What points at this work

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

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General Framework for AI Security and Privacy
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Defense Methods for Adversarial Attacks and Privacy Issues in Secure AI
inferred← citeshttps://doi.org/10.1007/978-3-031-99447-0_2openalex-snapshot
Ethical Considerations and Regulatory Standards for Adversarial Defense
inferred← citeshttps://doi.org/10.1007/978-3-032-19099-4_32openalex-snapshot
An Object-Level Entropy-Based Adversarial Attack for Image Privacy
inferred← citeshttps://doi.org/10.1007/978-3-032-22208-4_22openalex-snapshot
Experimental Evaluation of Security Attacks on Self-driving Car Platforms
inferred← citeshttps://doi.org/10.1007/978-981-95-4109-6_5openalex-snapshot
TDAdv: Improving Transferability of Unrestricted Adversarial Examples with Text-Guided Diffusion
inferred← citeshttps://doi.org/10.1007/978-981-96-5527-4_115openalex-snapshot
Defending Against Adversarial Examples with Adaptive Quantization Table-Guided Image Compression
inferred← citeshttps://doi.org/10.1007/s10462-025-11373-wopenalex-snapshot
Securing (vision-based) autonomous systems: taxonomy, challenges, and defense mechanisms against adversarial threats
inferred← citeshttps://doi.org/10.1007/s11227-025-07480-8openalex-snapshot
FastMS-CDA: speeding up adversarial sample generation with multistage diffusion model
inferred← citeshttps://doi.org/10.1007/s11831-026-10609-4openalex-snapshot
Deep Learning for Vehicle Trajectory Prediction in Intelligent Transportation: Methods, Challenges, and Future Directions
inferred← citeshttps://doi.org/10.1016/j.cosrev.2025.100887openalex-snapshot
Revisiting workflow scheduling with the power of edge computing: Taxonomy, review, and open challenges
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.126752openalex-snapshot
Adaptive precision layering for efficient adversarial training of deep learning models in intelligent vehicles
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.127998openalex-snapshot
Towards robust and generalizable adversarial purification for deep image classification under unknown attacks
inferred← citeshttps://doi.org/10.1016/j.eswa.2026.133656openalex-snapshot
Adversarial semantic correction distillation with boundary-guided feature alignment for object detection
inferred← citeshttps://doi.org/10.1016/j.inffus.2026.104539openalex-snapshot
FEAP-DiT: Frequency-decoupled efficient adversarial purification with diffusion transformers
inferred← citeshttps://doi.org/10.1016/j.iot.2026.101980openalex-snapshot
A unified interactive simulation platform for smart mobility: Adversarial attack generation in connected autonomous vehicles
inferred← citeshttps://doi.org/10.1016/j.jisa.2024.103960openalex-snapshot
Workplace security and privacy implications in the GenAI age: A survey
inferred← citeshttps://doi.org/10.1016/j.jss.2026.112796openalex-snapshot
Ache-Fuzz: Constraint-aware fuzzing for vulnerability discovery in distributed deep learning frameworks
inferred← citeshttps://doi.org/10.1016/j.mlwa.2026.100983openalex-snapshot
SCOPE-LM: Single Cell Context-Conditioned and Pathway-Enhanced Language Model for Cell Type Annotation
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.133591openalex-snapshot
Reversible adversarial examples generation based on coverless information hiding in deep learning model
inferred← citeshttps://doi.org/10.1016/j.neucom.2026.135003openalex-snapshot
A survey on assessing the generalization area of neural networks: Uncertainty, out-of-distribution and adversarial samples
inferred← citeshttps://doi.org/10.1016/j.rineng.2025.105177openalex-snapshot
Black-box adversarial attack defense approach: An empirical analysis from cybersecurity perceptive
inferred← citeshttps://doi.org/10.1038/s41598-025-27691-6openalex-snapshot
Hybrid GNN–LSTM defense with differential privacy and secure multi-party computation for edge-optimized neuromorphic autonomous systems
inferred← citeshttps://doi.org/10.1080/13682199.2026.2644653openalex-snapshot
JPEG compression-resistant adversarial attack with invisible watermark embedding
inferred← citeshttps://doi.org/10.1080/24751839.2026.2730085openalex-snapshot
A benchmarking framework and its validation on all evasion attacks on image classifiers listed in the adversarial robustness toolbox
inferred← citeshttps://doi.org/10.1093/comjnl/bxag006openalex-snapshot
TDTCE: generating adversarial instances against NIDS via diffusion model
inferred← citeshttps://doi.org/10.1109/access.2025.3561717openalex-snapshot
Driver Drowsiness Detection Using Swin Transformer and Diffusion Models for Robust Image Denoising
inferred← citeshttps://doi.org/10.1109/access.2026.3727983openalex-snapshot
Adversarial Attacks in Autonomous Vehicles: A Comprehensive Review of Threats and Defence Strategies
inferred← citeshttps://doi.org/10.1109/apsec66846.2025.00051openalex-snapshot
Dynamic Variance Reduction-Based Reusable Test Case Generation for Image Classification
inferred← citeshttps://doi.org/10.1109/cogmi67134.2025.00018openalex-snapshot
Restricted Hopfield Networks are Resilient to Adversarial Perturbations
inferred← citeshttps://doi.org/10.1109/dsn-w65791.2025.00071openalex-snapshot
Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1109/flics70075.2026.11621963openalex-snapshot
Mitigating One-to-N Backdoor Attacks in Federated Learning
inferred← citeshttps://doi.org/10.1109/icc59461.2026.11588238openalex-snapshot
PHANTOM: PHysical ANamorphic Threats Obstructing Connected Vehicle Mobility
inferred← citeshttps://doi.org/10.1109/iccvw69036.2025.00123openalex-snapshot
Impact of Black-Box Adversarial Attacks on Deep Neural Networks for Skin Imaging
inferred← citeshttps://doi.org/10.1109/icefeet64463.2024.10867187openalex-snapshot
Navigating Image Classification: A Comprehensive Comparison of CNN and Transfer Learning Techniques
inferred← citeshttps://doi.org/10.1109/icidca66325.2025.11280530openalex-snapshot
Robust Adversarial Image Detection using Advanced Deep Learning Techniques: An Ensemble-based Approach
inferred← citeshttps://doi.org/10.1109/istas65609.2025.11269642openalex-snapshot
Adversarial Attacks against Traffic Sign Recognition: Taxonomy and Open Research Areas
inferred← citeshttps://doi.org/10.1109/iv64158.2025.11097424openalex-snapshot
CyberDet: Real-time Adversarial Attacks Detection for Autonomous Robots and Self-driving Cars
inferred← citeshttps://doi.org/10.1109/jiot.2024.3519599openalex-snapshot
Adversarial Lens Flares: A Threat to Camera-Based Systems in Smart Devices
inferred← citeshttps://doi.org/10.1109/lra.2025.3580923openalex-snapshot
Semantic Hierarchy-Guided Adversarial Attack for Autonomous Driving
inferred← citeshttps://doi.org/10.1109/nelex69209.2026.11590051openalex-snapshot
Evaluating the Robustness of YOLO-Based Object Detection Against Adversarial Attacks in Autonomous Vehicles
inferred← citeshttps://doi.org/10.1109/spw67851.2025.00045openalex-snapshot
Do Adversarial Patches Generalize? Attack Transferability Study Across Real-Time Segmentation Models in Autonomous Vehicles
inferred← citeshttps://doi.org/10.1109/tc.2026.3712120openalex-snapshot
Facilitating Fuzzing-Based Simulation Testing of Autonomous Driving System Through Inter-Vehicle Interaction Modeling
inferred← citeshttps://doi.org/10.1109/tcad.2025.3576320openalex-snapshot
SPLAT: Revisiting Latency Attack on Dynamic Neural Networks
inferred← citeshttps://doi.org/10.1109/tdsc.2026.3681562openalex-snapshot
Boosting the Stealthiness of Backdoor Attack Against Data-Free Detection
inferred← citeshttps://doi.org/10.1109/wacv61042.2026.00170openalex-snapshot
AD 2 : Analysis and Detection of Adversarial Threats in Visual Perception for End-to-End Autonomous Driving Systems
inferred← citeshttps://doi.org/10.1145/3743126openalex-snapshot
Adversarial Attacks of Vision Tasks in the Past 10 Years: A Survey
inferred← citeshttps://doi.org/10.1145/3773023openalex-snapshot
Distortion Classification in Computer Vision Applications: Current Progress, Challenges, and Perspectives
inferred← citeshttps://doi.org/10.1145/3785666openalex-snapshot
Energy-Latency Attacks: A New Adversarial Threat to Deep Learning
inferred← citeshttps://doi.org/10.1145/3828753openalex-snapshot
Deep Neural Networks Inspired by Differential Equations
inferred← citeshttps://doi.org/10.1360/ssi-2025-0205openalex-snapshot
A detection method against adversarial example attack based on neural network decision boundary and consistency analysis
inferred← citeshttps://doi.org/10.2139/ssrn.5387355openalex-snapshot
Adaptive Black-Box Adversarial Attack Based on Deep Reinforcement Learning Against Deep Learning Models
inferred← citeshttps://doi.org/10.29121/shodhkosh.v6.i2s.2025.6729openalex-snapshot
AI-BASED LEARNING SYSTEMS FOR PHOTOGRAPHY STUDENTS
inferred← citeshttps://doi.org/10.29121/shodhkosh.v6.i4s.2025.6841openalex-snapshot
AI-BASED NOISE REDUCTION IN ARTISTIC PHOTOGRAPHY
inferred← citeshttps://doi.org/10.3390/electronics14101971openalex-snapshot
Hash-Based Message Authentication Code with a Reverse Fuzzy Extractor for a CMOS Image Sensor
inferred← citeshttps://doi.org/10.3390/info17020182openalex-snapshot
Adversarial Example Generation Method Based on Wavelet Transform
inferred← citeshttps://doi.org/10.3390/jcp6050169openalex-snapshot
Robustness Evaluation of Lightweight YOLO Detectors for Traffic Monitoring Under Visual Corruptions and Adversarial Attacks
inferred← citeshttps://doi.org/10.3390/s25061870openalex-snapshot
Artificial Intelligence and Machine Learning in Sensing and Image Processing
inferred← citeshttps://doi.org/10.4018/979-8-3373-2200-1.ch001openalex-snapshot
The Dark Side of AI
inferred← citeshttps://doi.org/10.4018/979-8-3693-6135-1.ch009openalex-snapshot
Future Trends in AI Security
inferred← citeshttps://doi.org/10.62517/jike.202604219openalex-snapshot
A Survey of Deep Learning-Based Multi-Sensor Fusion for 3D Object Detection in Autonomous Driving
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