On the Adversarial Robustness of Vision Transformers

29 March 2021

Papers citing "On the Adversarial Robustness of Vision Transformers"

32 / 32 papers shown

Title
Examining the Threat Landscape: Foundation Models and Model Stealing Ankita Raj Deepankar Varma Chetan Arora AAML 66 1 0 25 Feb 2025
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions Runhao Zeng Xiaoyong Chen Jiaming Liang Huisi Wu Guangzhong Cao Yong Guo AAML 32 3 0 29 Mar 2024
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers Haoyang Liu Aditya Singh Yijiang Li Haohan Wang AAML ViT 31 1 0 15 Mar 2024
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks Lucas Beerens D. Higham 21 1 0 28 Nov 2023
What Makes Pre-Trained Visual Representations Successful for Robust Manipulation? Kaylee Burns Zach Witzel Jubayer Ibn Hamid Tianhe Yu Chelsea Finn Karol Hausman OOD SSL 20 22 0 03 Nov 2023
Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation Shashank Kotyan Danilo Vasconcellos Vargas ViT 22 2 0 01 Nov 2023
On the unreasonable vulnerability of transformers for image restoration -- and an easy fix Shashank Agnihotri Kanchana Vaishnavi Gandikota Julia Grabinski Paramanand Chandramouli M. Keuper 30 9 0 25 Jul 2023
Why Does Little Robustness Help? Understanding and Improving Adversarial Transferability from Surrogate Training Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin AAML 22 20 0 15 Jul 2023
Random Position Adversarial Patch for Vision Transformers Mingzhen Shao ViT AAML 14 2 0 09 Jul 2023
Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization Jianping Zhang Yizhan Huang Weibin Wu Michael R. Lyu AAML ViT 13 48 0 28 Mar 2023
A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking Chang-Shu Liu Yinpeng Dong Wenzhao Xiang X. Yang Hang Su Junyi Zhu YueFeng Chen Yuan He H. Xue Shibao Zheng OOD VLM AAML 15 72 0 28 Feb 2023
MedViT: A Robust Vision Transformer for Generalized Medical Image Classification Omid Nejati Manzari Hamid Ahmadabadi Hossein Kashiani S. B. Shokouhi Ahmad Ayatollahi ViT MedIm 18 174 0 19 Feb 2023
Efficiency 360: Efficient Vision Transformers Badri N. Patro Vijay Srinivas Agneeswaran 19 6 0 16 Feb 2023
Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense Zunzhi You Daochang Liu Bohyung Han Chang Xu AAML VLM 24 4 0 02 Feb 2023
Inference Time Evidences of Adversarial Attacks for Forensic on Transformers Hugo Lemarchant Liang Li Yiming Qian Yuta Nakashima Hajime Nagahara ViT AAML 22 0 0 31 Jan 2023
Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks I. Wasserman OOD MedIm AAML 6 0 0 26 Oct 2022
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data Ching-Yun Ko Pin-Yu Chen Jeet Mohapatra Payel Das Lucani E. Daniel 19 3 0 06 Oct 2022
On the interplay of adversarial robustness and architecture components: patches, convolution and attention Francesco Croce Matthias Hein 37 6 0 14 Sep 2022
Bag of Tricks for FGSM Adversarial Training Zichao Li Li Liu Zeyu Wang Yuyin Zhou Cihang Xie AAML 14 6 0 06 Sep 2022
Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective Gihyun Kim Juyeop Kim Jong-Seok Lee AAML ViT 6 4 0 20 Aug 2022
Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification Faris Almalik Mohammad Yaqub Karthik Nandakumar ViT AAML MedIm 10 33 0 04 Aug 2022
Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO Javier Rando Nasib Naimi Thomas Baumann Max Mathys AAML 9 5 0 14 Jun 2022
3D Common Corruptions and Data Augmentation Oğuzhan Fatih Kar Teresa Yeo Andrei Atanov Amir Zamir 3DPC 31 107 0 02 Mar 2022
How Do Vision Transformers Work? Namuk Park Songkuk Kim ViT 25 463 0 14 Feb 2022
Are Transformers More Robust? Towards Exact Robustness Verification for Transformers B. Liao Chih-Hong Cheng Hasan Esen Alois C. Knoll AAML 16 1 0 08 Feb 2022
Are Transformers More Robust Than CNNs? Yutong Bai Jieru Mei Alan Yuille Cihang Xie ViT AAML 178 257 0 10 Nov 2021
Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation Yao Qin Chiyuan Zhang Ting Chen Balaji Lakshminarayanan Alex Beutel Xuezhi Wang ViT 39 42 0 15 Oct 2021
Certified Patch Robustness via Smoothed Vision Transformers Hadi Salman Saachi Jain Eric Wong Aleksander Mkadry AAML 57 58 0 11 Oct 2021
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs Philipp Benz Soomin Ham Chaoning Zhang Adil Karjauv In So Kweon AAML ViT 29 78 0 06 Oct 2021
Intriguing Properties of Vision Transformers Muzammal Naseer Kanchana Ranasinghe Salman Khan Munawar Hayat F. Khan Ming-Hsuan Yang ViT 251 618 0 21 May 2021
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 239 2,592 0 04 May 2021
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 256 3,108 0 04 Nov 2016