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Direction-aggregated Attack for Transferable Adversarial Examples

https://doi.org/10.1145/3501769
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What this work says

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

inferredcites →https://doi.org/10.1007/978-3-030-01264-9_28openalex-snapshot
Transferable Adversarial Perturbations
inferredcites →https://doi.org/10.1016/j.protcy.2014.09.007openalex-snapshot
First Steps Towards an Intelligent Laser Welding Architecture Using Deep Neural Networks and Reinforcement Learning
inferredcites →https://doi.org/10.1016/s0898-1221(96)90463-0openalex-snapshot
Advances in neural information processing systems 7
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.2016.282openalex-snapshot
DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2016.308openalex-snapshot
Rethinking the Inception Architecture for Computer Vision
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
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.2018.00957openalex-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.2019.00284openalex-snapshot
Improving Transferability of Adversarial Examples With Input Diversity
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.2019.00624openalex-snapshot
ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples
inferredcites →https://doi.org/10.1109/cvpr.2019.00723openalex-snapshot
Feature Space Perturbations Yield More Transferable Adversarial Examples
inferredcites →https://doi.org/10.1109/sp.2017.49openalex-snapshot
Towards Evaluating the Robustness of Neural Networks
inferredcites →https://doi.org/10.1145/1273496openalex-snapshot
Proceedings of the 24th international conference on Machine learning
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1201/9781351251389-8openalex-snapshot
Adversarial Examples in the Physical World
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/10.1609/aaai.v34i07.6810openalex-snapshot
Learning Transferable Adversarial Examples via Ghost Networks
inferredcites →https://doi.org/10.48550/arxiv.1102.0183openalex-snapshot
High-Performance Neural Networks for Visual Object Classification
inferredcites →https://doi.org/10.48550/arxiv.1312.6199openalex-snapshot
Intriguing properties of neural networks
inferredcites →https://doi.org/10.48550/arxiv.1412.6572openalex-snapshot
Explaining and Harnessing Adversarial Examples
inferredcites →https://doi.org/10.48550/arxiv.1611.02770openalex-snapshot
Delving into Transferable Adversarial Examples and Black-box Attacks
inferredcites →https://doi.org/10.48550/arxiv.1705.07204openalex-snapshot
Ensemble Adversarial Training: Attacks and Defenses
inferredcites →https://doi.org/10.48550/arxiv.1706.06083openalex-snapshot
Towards Deep Learning Models Resistant to Adversarial Attacks
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.1809.03113openalex-snapshot
Certified Adversarial Robustness with Additive Noise

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.22808openalex-snapshot
Attention‐guided transformation‐invariant attack for black‐box adversarial examples
inferred← citeshttps://doi.org/10.1007/s10489-024-05728-zopenalex-snapshot
Improving the transferability of adversarial attacks via self-ensemble
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.111729openalex-snapshot
Feature-aware transferable adversarial attacks against image classification
inferred← citeshttps://doi.org/10.1016/j.cose.2022.102811openalex-snapshot
Improving transferability of adversarial examples by saliency distribution and data augmentation
inferred← citeshttps://doi.org/10.1016/j.csi.2022.103693openalex-snapshot
Improving transferability of adversarial examples with powerful affine-shear transformation attack
inferred← citeshttps://doi.org/10.1109/cvprw56347.2022.00030openalex-snapshot
Exploring Robustness Connection between Artificial and Natural Adversarial Examples
inferred← citeshttps://doi.org/10.1109/iccv51070.2023.00419openalex-snapshot
How to choose your best allies for a transferable attack?
inferred← citeshttps://doi.org/10.1109/jiot.2024.3373461openalex-snapshot
Harmonizing Transferability and Imperceptibility: A Novel Ensemble Adversarial Attack
inferred← citeshttps://doi.org/10.1109/tmm.2024.3428311openalex-snapshot
Adaptive Multi-scale Degradation-Based Attack for Boosting the Adversarial Transferability
inferred← citeshttps://doi.org/10.1609/aaai.v37i8.26139openalex-snapshot
Training Meta-Surrogate Model for Transferable Adversarial Attack
inferred← citeshttps://doi.org/10.21203/rs.3.rs-4178736/v1openalex-snapshot
Side Channel Based Substitute Adversarial Attack on Deep Learning Models
inferred← citeshttps://doi.org/10.48550/arxiv.2110.00623openalex-snapshot
Calibrated Adversarial Training
inferred← citeshttps://doi.org/10.70675/690e25b2z1fb9z4d7fza6cbzcb09e2b773cfopenalex-snapshot
Neural networks security under realistic scenario
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