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Empowering Physical Attacks with Jacobian Matrix Regularization on Vit-Based Detectors

https://doi.org/10.2139/ssrn.4690907
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inferredcites →https://doi.org/10.1007/978-3-031-20053-3_5openalex-extracted
DaViT: Dual Attention Vision Transformers
inferredcites →https://doi.org/10.1007/978-3-031-20077-9_17openalex-extracted
Exploring Plain Vision Transformer Backbones for Object Detection
inferredcites →https://doi.org/10.1016/j.neunet.2023.03.041openalex-extracted
Differential evolution based dual adversarial camouflage: Fooling human eyes and object detectors
inferredcites →https://doi.org/10.1016/j.neunet.2023.04.012openalex-extracted
Remix: Towards the transferability of adversarial examples
inferredcites →https://doi.org/10.1016/j.neunet.2023.06.031openalex-extracted
Strengthening transferability of adversarial examples by adaptive inertia and amplitude spectrum dropout
inferredcites →https://doi.org/10.1016/j.patcog.2021.108491openalex-extracted
Relevance attack on detectors
inferredcites →https://doi.org/10.1016/j.patcog.2023.109435openalex-extracted
Boosting transferability of physical attack against detectors by redistributing separable attention
inferredcites →https://doi.org/10.1016/j.patcog.2023.109828openalex-extracted
Revisiting the transferability of adversarial examples via source-agnostic adversarial feature inducing method
inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-extracted
Feature Pyramid Networks for Object Detection
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01480openalex-extracted
Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness
inferredcites →https://doi.org/10.1109/iccv37128.2017openalex-extracted
2017 IEEE International Conference on Computer Vision (ICCV)
inferredcites →https://doi.org/10.1109/iccv48922.2021.00061openalex-extracted
Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions
inferredcites →https://doi.org/10.1109/iccv48922.2021.00986openalex-extracted
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
inferredcites →https://doi.org/10.1109/tip.2023.3251719openalex-extracted
Erosion Attack: Harnessing Corruption To Improve Adversarial Examples
inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-extracted
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-extracted
Focal Loss for Dense Object Detection
inferredcites →https://doi.org/10.1109/tpami.2021.3119563openalex-extracted
Detection and Tracking Meet Drones Challenge
inferredcites →https://doi.org/10.1109/tpami.2023.3341806openalex-extracted
CrossFormer++: A Versatile Vision Transformer Hinging on Cross-Scale Attention
inferredcites →https://doi.org/10.1609/aaai.v36i1.19936openalex-extracted
Learning to Learn Transferable Attack
inferredcites →https://doi.org/10.1609/aaai.v36i2.20141openalex-extracted
FCA: Learning a 3D Full-Coverage Vehicle Camouflage for Multi-View Physical Adversarial Attack
inferredcites →https://doi.org/10.1609/aaai.v36i3.20169openalex-extracted
Towards Transferable Adversarial Attacks on Vision Transformers
inferredcites →https://doi.org/10.4230/oasics.ng-res.2024.2openalex-extracted
A Multi-Modal Distributed Real-Time IoT System for Urban Traffic Control (Invited Paper)
inferredcites →https://doi.org/10.48550/arxiv.1908.06281openalex-extracted
Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks
inferredcites →https://doi.org/10.48550/arxiv.2002.05990openalex-extracted
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
inferredcites →https://doi.org/10.48550/arxiv.2006.04710openalex-extracted
The Lipschitz Constant of Self-Attention
inferredcites →https://doi.org/10.48550/arxiv.2010.11929openalex-extracted
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
inferredcites →https://doi.org/10.48550/arxiv.2107.08430openalex-extracted
YOLOX: Exceeding YOLO Series in 2021
inferredcites →https://doi.org/10.48550/arxiv.2111.10659openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2202.03599openalex-extracted
Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.2203.13639openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2203.16527openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2304.06718openalex-extracted
inferredcites →https://doi.org/10.1007/978-3-031-19775-8_24openalex-snapshot
Are Vision Transformers Robust to Patch Perturbations?
inferredcites →https://doi.org/10.52202/075280-0868openalex-snapshot
Segment Everything Everywhere All at Once
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