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A Universal Defense Strategy Against Adversarial Attacks Based on Attention-Guided

https://doi.org/10.2139/ssrn.4850545
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/int.22808openalex-extracted
Attention‐guided transformation‐invariant attack for black‐box adversarial examples
inferredcites →https://doi.org/10.1007/978-3-030-01234-2_1openalex-extracted
CBAM: Convolutional Block Attention Module
inferredcites →https://doi.org/10.1007/978-3-030-58621-8_45openalex-extracted
Contrastive Multiview Coding
inferredcites →https://doi.org/10.1016/j.inffus.2021.05.005openalex-extracted
A defense method based on attention mechanism against traffic sign adversarial samples
inferredcites →https://doi.org/10.1016/j.patrec.2019.06.028openalex-extracted
Generate adversarial examples by spatially perturbing on the meaningful area
inferredcites →https://doi.org/10.1109/cvpr.2018.00745openalex-extracted
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-extracted
Non-local Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00445openalex-extracted
Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses
inferredcites →https://doi.org/10.1109/cvpr.2019.01171openalex-extracted
Adversarial Defense by Stratified Convolutional Sparse Coding
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00129openalex-extracted
Achieving Robustness in the Wild via Adversarial Mixing With Disentangled Representations
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01155openalex-extracted
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00681openalex-extracted
Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01283openalex-extracted
Scaled-YOLOv4: Scaling Cross Stage Partial Network
inferredcites →https://doi.org/10.1109/iccv.2019.00673openalex-extracted
Bilateral Adversarial Training: Towards Fast Training of More Robust Models Against Adversarial Attacks
inferredcites →https://doi.org/10.1109/sp.2016.41openalex-extracted
Distillation as a Defense to Adversarial Perturbations Against Deep Neural Networks
inferredcites →https://doi.org/10.1109/tip.2019.2940533openalex-extracted
Image Super-Resolution as a Defense Against Adversarial Attacks
inferredcites →https://doi.org/10.1109/tip.2021.3127849openalex-extracted
Universal Adversarial Patch Attack for Automatic Checkout Using Perceptual and Attentional Bias
inferredcites →https://doi.org/10.1109/tip.2021.3127851openalex-extracted
Remote Sensing Scene Classification via Multi-Branch Local Attention Network
inferredcites →https://doi.org/10.1109/tnnls.2020.2979670openalex-extracted
A Survey of the Usages of Deep Learning for Natural Language Processing
inferredcites →https://doi.org/10.1109/tnnls.2021.3113342openalex-extracted
Feature Distillation in Deep Attention Network Against Adversarial Examples
inferredcites →https://doi.org/10.1109/tpami.2020.3033291openalex-extracted
Universal Adversarial Attack on Attention and the Resulting Dataset DAmageNet
inferredcites →https://doi.org/10.1109/wacv.2018.00097openalex-extracted
Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks
inferredcites →https://doi.org/10.1109/wacv48630.2021.00318openalex-extracted
Rotate to Attend: Convolutional Triplet Attention Module
inferredcites →https://doi.org/10.1145/3219819.3219910openalex-extracted
SHIELD
inferredcites →https://doi.org/10.1145/3422622openalex-extracted
Generative adversarial networks
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-extracted
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1412.5068openalex-extracted
Towards Deep Neural Network Architectures Robust to Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1709.01507openalex-extracted
Squeeze-and-Excitation Networks
inferredcites →https://doi.org/10.48550/arxiv.1802.06816openalex-extracted
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
inferredcites →https://doi.org/10.48550/arxiv.1805.06605openalex-extracted
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models
inferredcites →https://doi.org/10.48550/arxiv.1811.07457openalex-extracted
Generalizable Adversarial Training via Spectral Normalization
inferredcites →https://doi.org/10.48550/arxiv.1905.10626openalex-extracted
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness
inferredcites →https://doi.org/10.48550/arxiv.1912.10185openalex-extracted
Jacobian Adversarially Regularized Networks for Robustness
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