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ELAA: An efficient local adversarial attack using model interpreters

https://doi.org/10.1002/int.22680
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01258-8_10openalex-snapshot
Practical Black-Box Attacks on Deep Neural Networks Using Efficient Query Mechanisms
inferredcites →https://doi.org/10.1016/j.cose.2020.102111openalex-snapshot
Mask-guided noise restriction adversarial attacks for image classification
inferredcites →https://doi.org/10.1016/j.cose.2021.102220openalex-snapshot
FineFool: A novel DNN object contour attack on image recognition based on the attention perturbation adversarial technique
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1109/cvpr.2014.81openalex-snapshot
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2015.7298965openalex-snapshot
Fully convolutional networks for semantic segmentation
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
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.2019.00668openalex-snapshot
Curls & Whey: Boosting Black-Box Adversarial Attacks
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01291openalex-snapshot
Robust Superpixel-Guided Attentional Adversarial Attack
inferredcites →https://doi.org/10.1109/cvprw.2009.5206848openalex-snapshot
ImageNet: A large-scale hierarchical image database
inferredcites →https://doi.org/10.1109/eurosp.2016.36openalex-snapshot
The Limitations of Deep Learning in Adversarial Settings
inferredcites →https://doi.org/10.1109/iccv.2017.74openalex-snapshot
Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization
inferredcites →https://doi.org/10.1109/ijcnn48605.2020.9206626openalex-snapshot
Eigen-CAM: Class Activation Map using Principal Components
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/spw.2018.00009openalex-snapshot
Audio Adversarial Examples: Targeted Attacks on Speech-to-Text
inferredcites →https://doi.org/10.1109/tevc.2019.2890858openalex-snapshot
One Pixel Attack for Fooling Deep Neural Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2815688openalex-snapshot
Deeply Supervised Salient Object Detection with Short Connections
inferredcites →https://doi.org/10.1109/wacv.2018.00097openalex-snapshot
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/wacv45572.2020.9093360openalex-snapshot
Ablation-CAM: Visual Explanations for Deep Convolutional Network via Gradient-free Localization
inferredcites →https://doi.org/10.1117/1.3267105openalex-snapshot
Most apparent distortion: full-reference image quality assessment and the role of strategy
inferredcites →https://doi.org/10.1145/2939672.2939778openalex-snapshot
"Why Should I Trust You?"
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3394171.3413543openalex-snapshot
Learning Optimization-based Adversarial Perturbations for Attacking Sequential Recognition Models
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
inferredcites →https://doi.org/10.1371/journal.pone.0130140openalex-snapshot
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
inferredcites →https://doi.org/10.1609/aaai.v33i01.3301742openalex-snapshot
AutoZOOM: Autoencoder-Based Zeroth Order Optimization Method for Attacking Black-Box Neural Networks
inferredcites →https://doi.org/10.18653/v1/n16-3020openalex-snapshot
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties 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.1411.4038openalex-snapshot
Fully Convolutional Networks for Semantic Segmentation
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1412.6856openalex-snapshot
Object Detectors Emerge in Deep Scene CNNs
inferredcites →https://doi.org/10.48550/arxiv.1704.02685openalex-snapshot
Learning Important Features Through Propagating Activation Differences
inferredcites →https://doi.org/10.48550/arxiv.1908.01224openalex-snapshot
Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models
inferredcites →https://doi.org/10.48550/arxiv.1908.06281openalex-snapshot
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.2101.01032openalex-snapshot
Local Black-box Adversarial Attacks: A Query Efficient Approach
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images
inferredcites →https://doi.org/10.7544/issn1000-1239.2019.20190540openalex-snapshot
Survey on Techniques, Applications and Security of Machine Learning Interpretability

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.22889openalex-snapshot
Feature autoencoder for detecting adversarial examples
inferred← citeshttps://doi.org/10.1002/int.22892openalex-snapshot
Attention‐guided black‐box adversarial attacks with large‐scale multiobjective evolutionary optimization
inferred← citeshttps://doi.org/10.1002/int.22894openalex-snapshot
Robust flocking of multiple intelligent agents with multiple disturbances
inferred← citeshttps://doi.org/10.1002/int.22939openalex-snapshot
Image splicing forgery detection by combining synthetic adversarial networks and hybrid dense U‐net based on multiple spaces
inferred← citeshttps://doi.org/10.1002/int.22947openalex-snapshot
A data variability index: Quantifying complexity of models and analyzing adversarial data
inferred← citeshttps://doi.org/10.1002/int.22997openalex-snapshot
An effective and practical gradient inversion attack
inferred← citeshttps://doi.org/10.1002/int.23082openalex-snapshot
Contrastive hashing with vision transformer for image retrieval
inferred← citeshttps://doi.org/10.1016/j.cose.2022.102943openalex-snapshot
Visually imperceptible adversarial patch attacks
inferred← citeshttps://doi.org/10.1016/j.jisa.2025.104172openalex-snapshot
Decision attribution and local extremum-guided black-box adversarial attack with adjustable sparsity and discreteness
inferred← citeshttps://doi.org/10.1016/j.neucom.2023.126580openalex-snapshot
Learning relationship-preserving representation for multi-task adversarial attacks
inferred← citeshttps://doi.org/10.1016/j.patcog.2025.111652openalex-snapshot
Cross-task and time-aware adversarial attack framework for perception of autonomous driving
inferred← citeshttps://doi.org/10.1109/cvprw67362.2025.00343openalex-snapshot
EL-Attack: Explicit and Latent Space Hybrid Optimization based General and Effective Attack for Autonomous Driving Trajectory Prediction
inferred← citeshttps://doi.org/10.1109/globecom59602.2025.11432049openalex-snapshot
Unified CSI Reconstruction and Prediction with Channel Attention Mechanism
inferred← citeshttps://doi.org/10.3390/s22093445openalex-snapshot
A Universal Detection Method for Adversarial Examples and Fake Images
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