{"total":100,"rows":[{"id":"2202.06709","title":"How Do Vision Transformers Work?","created":"2022-02-14","cited_by_count":307,"area":null},{"id":"2204.12451","title":"Understanding The Robustness in Vision Transformers","created":"2022-04-26","cited_by_count":144,"area":null},{"id":"2203.01441","title":"3D Common Corruptions and Data Augmentation","created":"2022-03-02","cited_by_count":119,"area":null},{"id":"2302.09462","title":"MedViT: A Robust Vision Transformer for Generalized Medical Image Classification","created":"2023-02-19","cited_by_count":89,"area":null},{"id":"2302.14301","title":"A Comprehensive Study on Robustness of Image Classification Models: Benchmarking and Rethinking","created":"2023-02-28","cited_by_count":85,"area":null},{"id":"2209.07399","title":"A Light Recipe to Train Robust Vision Transformers","created":"2022-09-15","cited_by_count":64,"area":null},{"id":"2110.02797","title":"Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs","created":"2021-10-06","cited_by_count":57,"area":null},{"id":"2212.03862","title":"Teaching Matters: Investigating the Role of Supervision in Vision Transformers","created":"2022-12-07","cited_by_count":45,"area":null},{"id":"2303.15754","title":"Transferable Adversarial Attacks on Vision Transformers with Token Gradient Regularization","created":"2023-03-28","cited_by_count":42,"area":null},{"id":"2312.12444","title":"What Makes Pre-Trained Visual Representations Successful for Robust Manipulation?","created":"2023-11-03","cited_by_count":39,"area":null},{"id":"2307.07873","title":"Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability","created":"2023-07-15","cited_by_count":32,"area":null},{"id":"2304.11928","title":"Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving","created":"2023-04-24","cited_by_count":29,"area":null},{"id":"2402.03951","title":"Boosting Adversarial Transferability across Model Genus by Deformation-Constrained Warping","created":"2024-02-06","cited_by_count":18,"area":null},{"id":"2401.00529","title":"GraphGPT: Generative Pre-trained Graph Eulerian Transformer","created":"2023-12-31","cited_by_count":17,"area":null},{"id":"2208.02851","title":"Self-Ensembling Vision Transformer (SEViT) for Robust Medical Image Classification","created":"2022-08-04","cited_by_count":16,"area":null},{"id":"2301.10766","title":"On the Adversarial Robustness of Camera-based 3D Object Detection","created":"2023-01-25","cited_by_count":15,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2408.00496","title":"SegStitch: Multidimensional Transformer for Robust and Efficient Medical Imaging Segmentation","created":"2024-08-01","cited_by_count":13,"area":null},{"id":"2307.09763","title":"Towards Building More Robust Models with Frequency Bias","created":"2023-07-19","cited_by_count":12,"area":null},{"id":"2308.10315","title":"Improving Adversarial Robustness of Masked Autoencoders via Test-time Frequency-domain Prompting","created":"2023-08-20","cited_by_count":12,"area":null},{"id":"2403.20254","title":"Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions","created":"2024-03-29","cited_by_count":12,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2307.13856","title":"On the unreasonable vulnerability of transformers for image restoration -- and an easy fix","created":"2023-07-25","cited_by_count":11,"area":null},{"id":"2402.02629","title":"PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks","created":"2024-02-04","cited_by_count":11,"area":null},{"id":"2404.04452","title":"Vision transformers in domain adaptation and domain generalization: a study of robustness","created":"2024-04-05","cited_by_count":11,"area":null},{"id":"2210.06205","title":"On Divergence Measures for Bayesian Pseudocoresets","created":"2022-10-12","cited_by_count":10,"area":null},{"id":"2312.05716","title":"Initialization Matters for Adversarial Transfer Learning","created":"2023-12-10","cited_by_count":10,"area":null},{"id":"2405.16262","title":"Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency","created":"2024-05-25","cited_by_count":10,"area":null},{"id":"2210.08906","title":"A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunities","created":"2022-10-17","cited_by_count":9,"area":null},{"id":"2208.09602","title":"Exploring Adversarial Robustness of Vision Transformers in the Spectral Perspective","created":"2022-08-20","cited_by_count":8,"area":null},{"id":"2211.14646","title":"Towards Improved Input Masking for Convolutional Neural Networks","created":"2022-11-26","cited_by_count":8,"area":null},{"id":"2307.12027","title":"On the Effectiveness of Spectral Discriminators for Perceptual Quality Improvement","created":"2023-07-22","cited_by_count":8,"area":null},{"id":"2407.08972","title":"Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness","created":"2024-07-12","cited_by_count":8,"area":null},{"id":"2502.11910","title":"Adversarial Alignment for LLMs Requires Simpler, Reproducible, and More Measurable Objectives","created":"2025-02-17","cited_by_count":8,"area":null},{"id":"2302.01056","title":"Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense","created":"2023-02-02","cited_by_count":7,"area":null},{"id":"2301.03110","title":"RobArch: Designing Robust Architectures against Adversarial Attacks","created":"2023-01-08","cited_by_count":6,"area":null},{"id":"2301.13803","title":"Fairness-aware Vision Transformer via Debiased Self-Attention","created":"2023-01-31","cited_by_count":6,"area":null},{"id":"2302.08374","title":"Efficiency 360: Efficient Vision Transformers","created":"2023-02-16","cited_by_count":6,"area":null},{"id":"2409.15264","title":"UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework","created":"2024-09-23","cited_by_count":6,"area":null},{"id":"2202.03932","title":"Are Transformers More Robust? Towards Exact Robustness Verification for Transformers","created":"2022-02-08","cited_by_count":5,"area":null},{"id":"2402.03317","title":"SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization","created":"2024-01-02","cited_by_count":5,"area":null},{"id":"2206.06761","title":"Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINO","created":"2022-06-14","cited_by_count":4,"area":null},{"id":"2209.06953","title":"On the interplay of adversarial robustness and architecture components: patches, convolution and attention","created":"2022-09-14","cited_by_count":4,"area":null},{"id":"2209.13113","title":"FG-UAP: Feature-Gathering Universal Adversarial Perturbation","created":"2022-09-27","cited_by_count":4,"area":null},{"id":"2505.16402","title":"AdvReal: Physical Adversarial Patch Generation Framework for Security Evaluation of Object Detection Systems","created":"2025-05-22","cited_by_count":4,"area":null},{"id":"2309.13245","title":"RBFormer: Improve Adversarial Robustness of Transformer by Robust Bias","created":"2023-09-23","cited_by_count":3,"area":null},{"id":"2406.01765","title":"Reproducibility Study on Adversarial Attacks Against Robust Transformer Trackers","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2407.01157","title":"Unaligning Everything: Or Aligning Any Text to Any Image in Multimodal Models","created":"2024-07-01","cited_by_count":3,"area":null},{"id":"2407.03946","title":"TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers","created":"2024-07-04","cited_by_count":3,"area":null},{"id":"2503.14751","title":"LipShiFT: A Certifiably Robust Shift-based Vision Transformer","created":"2025-03-18","cited_by_count":3,"area":null},{"id":"2505.14042","title":"Adversarially Pretrained Transformers May Be Universally Robust In-Context Learners","created":"2025-05-20","cited_by_count":3,"area":null},{"id":"2209.02684","title":"Bag of Tricks for FGSM Adversarial Training","created":"2022-09-06","cited_by_count":2,"area":null},{"id":"2210.02989","title":"SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data","created":"2022-10-06","cited_by_count":2,"area":null},{"id":"2307.04066","title":"Random Position Adversarial Patch for Vision Transformers","created":"2023-07-09","cited_by_count":2,"area":null},{"id":"2310.03707","title":"OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks","created":"2023-10-05","cited_by_count":2,"area":null},{"id":"2311.17128","title":"Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks","created":"2023-11-28","cited_by_count":2,"area":null},{"id":"2401.01750","title":"Towards Robust Semantic Segmentation against Patch-based Attack via Attention Refinement","created":"2024-01-03","cited_by_count":2,"area":null},{"id":"2407.00389","title":"Query-Efficient Hard-Label Black-Box Attack against Vision Transformers","created":"2024-06-29","cited_by_count":2,"area":null},{"id":"2409.13828","title":"ViTGuard: Attention-aware Detection against Adversarial Examples for Vision Transformer","created":"2024-09-20","cited_by_count":2,"area":null},{"id":"2409.19850","title":"SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers","created":"2024-09-30","cited_by_count":2,"area":null},{"id":"2502.18077","title":"Examining the Threat Landscape: Foundation Models and Model Stealing","created":"2025-02-25","cited_by_count":2,"area":null},{"id":"2506.05394","title":"Attacking Attention of Foundation Models Disrupts Downstream Tasks","created":"2025-06-03","cited_by_count":2,"area":null},{"id":"2107.04827","title":"Identifying Layers Susceptible to Adversarial Attacks","created":"2021-07-10","cited_by_count":1,"area":null},{"id":"2210.13235","title":"Chaos Theory and Adversarial Robustness","created":"2022-10-20","cited_by_count":1,"area":null},{"id":"2306.05297","title":"Connectional-Style-Guided Contextual Representation Learning for Brain Disease Diagnosis","created":"2023-06-08","cited_by_count":1,"area":null},{"id":"2311.00441","title":"Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation","created":"2023-11-01","cited_by_count":1,"area":null},{"id":"2311.12914","title":"Attention Deficit is Ordered! Fooling Deformable Vision Transformers with Collaborative Adversarial Patches","created":"2023-11-21","cited_by_count":1,"area":null},{"id":"2402.01220","title":"Delving into Decision-based Black-box Attacks on Semantic Segmentation","created":"2024-02-02","cited_by_count":1,"area":null},{"id":"2404.00185","title":"On Inherent Adversarial Robustness of Active Vision Systems","created":"2024-03-29","cited_by_count":1,"area":null},{"id":"2407.17587","title":"S-E Pipeline: A Vision Transformer (ViT) based Resilient Classification Pipeline for Medical Imaging Against Adversarial Attacks","created":"2024-07-23","cited_by_count":1,"area":null},{"id":"2408.01139","title":"Interpreting Global Perturbation Robustness of Image Models using Axiomatic Spectral Importance Decomposition","created":"2024-08-02","cited_by_count":1,"area":null},{"id":"2412.18718","title":"Evaluating the Adversarial Robustness of Detection Transformers","created":"2024-12-25","cited_by_count":1,"area":null},{"id":"2506.06992","title":"Boosting Adversarial Transferability via Commonality-Oriented Gradient Optimization","created":"2025-06-08","cited_by_count":1,"area":null},{"id":"2508.14079","title":"A Guide to Robust Generalization: The Impact of Architecture, Pre-training, and Optimization Strategy","created":"2025-08-12","cited_by_count":1,"area":null},{"id":"2602.14318","title":"In Transformer We Trust? A Perspective on Transformer Architecture Failure Modes","created":"2026-02-15","cited_by_count":1,"area":null},{"id":"2604.19724","title":"Benign Overfitting in Adversarial Training for Vision Transformers","created":"2026-04-21","cited_by_count":1,"area":null},{"id":"2202.12435","title":"Understanding Adversarial Robustness from Feature Maps of Convolutional Layers","created":"2022-02-25","cited_by_count":0,"area":null},{"id":"2210.14405","title":"Adversarially Robust Medical Classification via Attentive Convolutional Neural Networks","created":"2022-10-26","cited_by_count":0,"area":null},{"id":"2211.13171","title":"Query Efficient Cross-Dataset Transferable Black-Box Attack on Action Recognition","created":"2022-11-23","cited_by_count":0,"area":null},{"id":"2301.13356","title":"Inference Time Evidences of Adversarial Attacks for Forensic on Transformers","created":"2023-01-31","cited_by_count":0,"area":null},{"id":"2310.11597","title":"The Efficacy of Transformer-based Adversarial Attacks in Security Domains","created":"2023-10-17","cited_by_count":0,"area":null},{"id":"2312.11466","title":"Extracting Interpretable Local and Global Representations from Attention on Time Series","created":"2023-09-16","cited_by_count":0,"area":null},{"id":"2402.08070","title":"Multi-Attribute Vision Transformers are Efficient and Robust Learners","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2403.10476","title":"Approximate Nullspace Augmented Finetuning for Robust Vision Transformers","created":"2024-03-15","cited_by_count":0,"area":null},{"id":"2412.14231","title":"ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods","created":"2024-12-18","cited_by_count":0,"area":null},{"id":"2412.20006","title":"Adversarial Robustness for Deep Learning-based Wildfire Prediction Models","created":"2024-12-28","cited_by_count":0,"area":null},{"id":"2501.03782","title":"Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights","created":"2025-01-07","cited_by_count":0,"area":null},{"id":"2506.22602","title":"Are Fast Methods Stable in Adversarially Robust Transfer Learning?","created":"2025-06-27","cited_by_count":0,"area":null},{"id":"2507.14248","title":"Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack","created":"2025-07-18","cited_by_count":0,"area":null},{"id":"2509.06459","title":"IGAff: Benchmarking Adversarial Iterative and Genetic Affine Algorithms on Deep Neural Networks","created":"2025-09-08","cited_by_count":0,"area":null},{"id":"2509.23806","title":"Influence-Guided Concolic Testing of Transformer Robustness","created":"2025-09-28","cited_by_count":0,"area":null},{"id":"2511.18562","title":"Ensuring Calibration Robustness in Split Conformal Prediction Under Adversarial Attacks","created":"2025-11-23","cited_by_count":0,"area":null},{"id":"2512.02830","title":"Defense That Attacks: How Robust Models Become Better Attackers","created":"2025-12-02","cited_by_count":0,"area":null},{"id":"2604.00605","title":"Fluently Lying: Adversarial Robustness Can Be Substrate-Dependent","created":"2026-04-01","cited_by_count":0,"area":null},{"id":"2605.18868","title":"DarkLLM: Learning Language-Driven Adversarial Attacks with Large Language Models","created":"2026-05-15","cited_by_count":0,"area":null},{"id":"2606.00738","title":"SORA: Free Second-Order Attacks in Fast Adversarial Training","created":"2026-05-30","cited_by_count":0,"area":null},{"id":"2607.07922","title":"Adversarial Decoys: Misdirecting Attention-Based Defenses in ViT","created":"2026-07-08","cited_by_count":0,"area":null},{"id":"2609.03453","title":"Preprocessing Failure and Adversarial Detection in Depthwise-Separable Edge Vision Systems","created":"2026-09-03","cited_by_count":0,"area":null},{"id":"2609.26081","title":"Margin-Drop Coordinates for Cross-Budget Robustness Evaluation","created":"2026-08-20","cited_by_count":0,"area":null},{"id":"2609.33513","title":"Printability-Constrained Adversarial Decals for Near-Nadir Aerial Perception: Measured Ink Gamuts, Nested Realism Constraints, and a Physical-World Bound","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}