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An efficient framework for generating robust adversarial examples
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1016/j.jpdc.2019.03.003openalex-snapshot
The security of machine learning in an adversarial setting: A survey
inferredcites →https://doi.org/10.1109/cec.2008.4631255openalex-snapshot
Natural Evolution Strategies
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.2019.00443openalex-snapshot
Adversarial Attacks Beyond the Image Space
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.2019.00706openalex-snapshot
MeshAdv: Adversarial Meshes for Visual Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00945openalex-snapshot
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks
inferredcites →https://doi.org/10.1109/cvprw.2019.00012openalex-snapshot
Fooling Automated Surveillance Cameras: Adversarial Patches to Attack Person Detection
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1609/aaai.v33i01.3301742openalex-snapshot
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/int.22510openalex-snapshot
Advanced evasion attacks and mitigations on practical ML‐based phishing website classifiers
inferred← citeshttps://doi.org/10.1002/int.22808openalex-snapshot
Attention‐guided transformation‐invariant attack for black‐box adversarial examples
inferred← citeshttps://doi.org/10.1002/int.22850openalex-snapshot
A data‐driven adversarial examples recognition framework via adversarial feature genomes
inferred← citeshttps://doi.org/10.1002/int.22851openalex-snapshot
D etect S ec : Evaluating the robustness of object detection models to adversarial attacks
inferred← citeshttps://doi.org/10.1007/978-981-99-9119-8_2openalex-snapshot
Blind Adversarial Training: Towards Comprehensively Robust Models Against Blind Adversarial Attacks
inferred← citeshttps://doi.org/10.1007/s00521-024-09760-5openalex-snapshot
UCTT: universal and low-cost adversarial example generation for tendency classification
inferred← citeshttps://doi.org/10.1016/j.csi.2021.103612openalex-snapshot
An efficient adversarial example generation algorithm based on an accelerated gradient iterative fast gradient
inferred← citeshttps://doi.org/10.1016/j.ins.2022.04.003openalex-snapshot
Similarity-based integrity protection for deep learning systems
inferred← citeshttps://doi.org/10.1016/j.jobe.2024.111493openalex-snapshot
Optimizing energy efficiency, thermal comfort, and indoor air quality in HVAC systems using a robust DRL algorithm
inferred← citeshttps://doi.org/10.1063/5.0223034openalex-snapshot
Identification method for micro–nano defects in Si3N4 ceramic bearing rollers
inferred← citeshttps://doi.org/10.1109/tdsc.2022.3143566openalex-snapshot
Attacking Deep Reinforcement Learning With Decoupled Adversarial Policy
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