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Attention‐guided transformation‐invariant attack for black‐box adversarial examples

https://doi.org/10.1002/int.22808
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What this work says

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

inferredcites →https://doi.org/10.1002/int.22258openalex-snapshot
Defense against adversarial attacks by low‐level image transformations
inferredcites →https://doi.org/10.1002/int.22267openalex-snapshot
An efficient framework for generating robust adversarial examples
inferredcites →https://doi.org/10.1002/int.22499openalex-snapshot
A lightweight 3D convolutional neural network for deepfake detection
inferredcites →https://doi.org/10.1007/978-3-030-01264-9_28openalex-snapshot
Transferable Adversarial Perturbations
inferredcites →https://doi.org/10.1007/978-3-030-58517-4_15openalex-snapshot
Yet Another Intermediate-Level Attack
inferredcites →https://doi.org/10.1007/978-3-319-51289-1_1openalex-snapshot
Media Convergence Meets Deconvergence
inferredcites →https://doi.org/10.1007/978-981-33-4149-4_5openalex-snapshot
Prospects for Media Convergence
inferredcites →https://doi.org/10.1016/j.patcog.2021.107950openalex-snapshot
PRRNet: Pixel-Region relation network for face forgery detection
inferredcites →https://doi.org/10.1109/access.2018.2807385openalex-snapshot
Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.319openalex-snapshot
Learning Deep Features for Discriminative Localization
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00678openalex-snapshot
Real-World Anomaly Detection in Surveillance Videos
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/cvpr.2019.00059openalex-snapshot
Feature Denoising for Improving Adversarial Robustness
inferredcites →https://doi.org/10.1109/cvpr.2019.00284openalex-snapshot
Improving Transferability of Adversarial Examples With Input Diversity
inferredcites →https://doi.org/10.1109/cvpr.2019.00723openalex-snapshot
Feature Space Perturbations Yield More Transferable Adversarial Examples
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00072openalex-snapshot
Towards Transferable Targeted Attack
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00124openalex-snapshot
Boosting the Transferability of Adversarial Samples via Attention
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00568openalex-snapshot
LAFEAT: Piercing Through Adversarial Defenses with Latent Features
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/iccv.2019.00483openalex-snapshot
Enhancing Adversarial Example Transferability With an Intermediate Level Attack
inferredcites →https://doi.org/10.1109/iccv.2019.00816openalex-snapshot
FDA: Feature Disruptive Attack
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1109/tip.2012.2207397openalex-snapshot
Multimodal Graph-Based Reranking for Web Image Search
inferredcites →https://doi.org/10.1109/tip.2017.2765836openalex-snapshot
Person Re-Identification With Metric Learning Using Privileged Information
inferredcites →https://doi.org/10.1109/tkde.2021.3117003openalex-snapshot
Discriminative Feature Mining Based on Frequency Information and Metric Learning for Face Forgery Detection
inferredcites →https://doi.org/10.1109/tmm.2012.2185041openalex-snapshot
Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification
inferredcites →https://doi.org/10.1145/3052973.3053009openalex-snapshot
Practical Black-Box Attacks against Machine Learning
inferredcites →https://doi.org/10.1145/3219819.3219903openalex-snapshot
EANN
inferredcites →https://doi.org/10.1145/3501769openalex-snapshot
Direction-aggregated Attack for Transferable Adversarial Examples
inferredcites →https://doi.org/10.1515/nor-2017-0209openalex-snapshot
Researching Media Convergence and Crossmedia News Production
inferredcites →https://doi.org/10.1609/aaai.v31i1.11231openalex-snapshot
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
inferredcites →https://doi.org/10.5860/choice.33-4484openalex-snapshot
The Handbook of brain theory and neural networks

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4850545openalex-extracted
A Universal Defense Strategy Against Adversarial Attacks Based on Attention-Guided
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/10.1007/s10586-024-04513-4openalex-snapshot
Deep learning adversarial attacks and defenses on license plate recognition system
inferred← citeshttps://doi.org/10.1016/j.dajour.2022.100153openalex-snapshot
An explainable deep-learning approach for job cycle time prediction
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/10.1038/s41598-025-89267-8openalex-snapshot
Universal attention guided adversarial defense using feature pyramid and non-local mechanisms
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3615158openalex-snapshot
ASGA: Attention-Based Sparse Global Attack to Video Action Recognition
inferred← citeshttps://doi.org/10.1109/tetci.2022.3214627openalex-snapshot
SA-ES: Subspace Activation Evolution Strategy for Black-Box Adversarial Attacks
inferred← citeshttps://doi.org/10.1111/exsy.70084openalex-snapshot
A Method for Extracting Black Box Models Based on Interpretable Attention
inferred← citeshttps://doi.org/10.1145/3763003openalex-snapshot
PEAS: A Strategy for Crafting Transferable Adversarial Examples
inferred← citeshttps://doi.org/10.21203/rs.3.rs-3221027/v1openalex-snapshot
Adversarial system of one-pixel attack for hyperspectral image classification
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