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Give Me Your Attention: Dot-Product Attention Considered Harmful for Adversarial Patch Robustness

https://doi.org/10.1109/cvpr52688.2022.01480
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What this work says

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

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End-to-End Object Detection with Transformers
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Microsoft COCO: Common Objects in Context
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ImageNet Large Scale Visual Recognition Challenge
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Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
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SceneFormer: Indoor Scene Generation with Transformers
inferredcites →https://doi.org/10.1109/cvpr.2018.00911openalex-snapshot
End-to-End Dense Video Captioning with Masked Transformer
inferredcites →https://doi.org/10.1109/cvpr.2018.00957openalex-snapshot
Boosting Adversarial Attacks with Momentum
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00583openalex-snapshot
Learning Texture Transformer Network for Image Super-Resolution
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00681openalex-snapshot
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inferredcites →https://doi.org/10.1109/cvpr46437.2021.00863openalex-snapshot
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inferredcites →https://doi.org/10.1109/cvpr46437.2021.01212openalex-snapshot
Pre-Trained Image Processing Transformer
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01268openalex-snapshot
Taming Transformers for High-Resolution Image Synthesis
inferredcites →https://doi.org/10.1109/cvpr52688.2022.00320openalex-snapshot
Video Swin Transformer
inferredcites →https://doi.org/10.1109/cvpr52688.2022.01716openalex-snapshot
Uformer: A General U-Shaped Transformer for Image Restoration
inferredcites →https://doi.org/10.1109/iccv.2019.00756openalex-snapshot
VideoBERT: A Joint Model for Video and Language Representation Learning
inferredcites →https://doi.org/10.1109/iccv48922.2021.00676openalex-snapshot
ViViT: A Video Vision Transformer
inferredcites →https://doi.org/10.1109/iccv48922.2021.00717openalex-snapshot
Segmenter: Transformer for Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv48922.2021.00774openalex-snapshot
On the Robustness of Vision Transformers to Adversarial Examples
inferredcites →https://doi.org/10.1109/iccv48922.2021.00986openalex-snapshot
Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
inferredcites →https://doi.org/10.1109/iccv48922.2021.01007openalex-snapshot
Understanding Robustness of Transformers for Image Classification
inferredcites →https://doi.org/10.1109/iccvw54120.2021.00210openalex-snapshot
SwinIR: Image Restoration Using Swin Transformer
inferredcites →https://doi.org/10.1109/iccvw54120.2021.00355openalex-snapshot
Video Transformer Network
inferredcites →https://doi.org/10.1609/aaai.v36i3.20169openalex-snapshot
Towards Transferable Adversarial Attacks on Vision Transformers
inferredcites →https://doi.org/10.1609/aaai.v36i6.20595openalex-snapshot
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks
inferredcites →https://doi.org/10.18653/v1/2022.naacl-main.319openalex-snapshot
FNet: Mixing Tokens with Fourier Transforms
inferredcites →https://doi.org/10.18653/v1/d15-1166openalex-snapshot
Effective Approaches to Attention-based Neural Machine Translation
inferredcites →https://doi.org/10.4230/lipics.itp.2023.19openalex-snapshot
Exploiting Generative AI to Scale up Intelligent Tutoring Systems
inferredcites →https://doi.org/10.4230/oasics.saia.2024.1openalex-snapshot
On Assessing ML Model Robustness: A Methodological Framework (Academic Track)
inferredcites →https://doi.org/10.48550/arxiv.2001.03994openalex-snapshot
Fast is better than free: Revisiting adversarial training
inferredcites →https://doi.org/10.48550/arxiv.2006.04768openalex-snapshot
Conv-Linformer: Boosting Linformer's Performance with Convolution in Small-Scale Settings
inferredcites →https://doi.org/10.48550/arxiv.2012.04124openalex-snapshot
Parameter Efficient Multimodal Transformers for Video Representation Learning
inferredcites →https://doi.org/10.48550/arxiv.2012.09958openalex-snapshot
Toward Transformer-Based Object Detection
inferredcites →https://doi.org/10.48550/arxiv.2102.05095openalex-snapshot
Is Space-Time Attention All You Need for Video Understanding?
inferredcites →https://doi.org/10.48550/arxiv.2105.10497openalex-snapshot
Intriguing Properties of Vision Transformers
inferredcites →https://doi.org/10.48550/arxiv.2110.04337openalex-snapshot
Adversarial Token Attacks on Vision Transformers
inferredcites →https://doi.org/10.5244/c.35.68openalex-snapshot
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs

What points at this work

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

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Directional Gradient Attacks for Inducing Controllable Detection Errors in DETR-Based Models
inferred← citeshttps://doi.org/10.1007/978-981-97-8685-5_39openalex-snapshot
Patch Attacks on Vision Transformer via Skip Attention Gradients
inferred← citeshttps://doi.org/10.1007/s11263-024-02120-9openalex-snapshot
Towards Robust Semantic Segmentation against Patch-Based Attack via Attention Refinement
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Universal and transferable attacks on pathology foundation models using microscopic perturbations
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inferred← citeshttps://doi.org/10.1109/apscon63569.2025.11144389openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/avss65446.2025.11149702openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvpr52729.2023.00400openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/cvpr52733.2024.01729openalex-snapshot
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions
inferred← citeshttps://doi.org/10.1109/icassp49357.2023.10096862openalex-snapshot
Defending Against Universal Patch Attacks by Restricting Token Attention in Vision Transformers
inferred← citeshttps://doi.org/10.1109/iccad66269.2025.11240783openalex-snapshot
Secure Token Pruning Mechanism and Accelerator for Vision Transformer
inferred← citeshttps://doi.org/10.1109/icmla61862.2024.00023openalex-snapshot
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inferred← citeshttps://doi.org/10.1109/smc58881.2025.11342643openalex-snapshot
ViTProbe: Defending Vision Transformers against Adversarial Patch Attacks through Single-Layer Inspection
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ImAdv: Transferable Implicit Adversarial Attack for 3D Object Detectors in Autonomous Driving
inferred← citeshttps://doi.org/10.1109/tcsvt.2026.3710546openalex-snapshot
AdaGuard: Location-Aware Adaptive Defense Against Adversarial Patch Attacks in Vision Transformers
inferred← citeshttps://doi.org/10.1109/tgrs.2024.3416685openalex-snapshot
Empowering Physical Attacks With Jacobian Matrix Regularization Against ViT-Based Detectors in UAV Remote Sensing Images
inferred← citeshttps://doi.org/10.1109/tifs.2026.3654868openalex-snapshot
The Chosen-Object Attack: Exploiting the Hungarian Matching Loss in Detection Transformers for Fun and Profit
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FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers
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An Introduction to Adversarially Robust Deep Learning
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A Closer Look at Robustness of Vision Transformers to Backdoor Attacks
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Adversarial Attention Deficit: Fooling Deformable Vision Transformers with Collaborative Adversarial Patches
inferred← citeshttps://doi.org/10.1109/wacv61041.2025.00721openalex-snapshot
When Visual State Space Model Meets Backdoor Attacks
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Mpox-PyramidTransferNet: A Hierarchical Transfer Learning Framework for Monkeypox and Dermatological Disease Classification
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MOSSNet: multiscale and oriented sorghum spike detection and counting in UAV images
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Double Attention: An Optimization Method for the Self-Attention Mechanism Based on Human Attention
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A Comprehensive Study on the Robustness of Deep Learning-Based Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking
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