{"total":720,"rows":[{"id":"2112.08304","title":"On the Convergence and Robustness of Adversarial Training","created":"2021-12-15","cited_by_count":130,"area":null},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2102.08452","title":"Globally-Robust Neural Networks","created":"2021-02-16","cited_by_count":108,"area":null},{"id":"2203.03818","title":"Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon","created":"2022-03-08","cited_by_count":101,"area":null},{"id":"2203.03373","title":"Adversarial Texture for Fooling Person Detectors in the Physical World","created":"2022-03-07","cited_by_count":96,"area":null},{"id":"2110.12976","title":"Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks","created":"2021-10-25","cited_by_count":91,"area":null},{"id":"2105.14944","title":"The effectiveness of feature attribution methods and its correlation with automatic evaluation scores","created":"2021-05-31","cited_by_count":88,"area":null},{"id":"2202.02643","title":"The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training","created":"2022-02-05","cited_by_count":85,"area":null},{"id":"2111.10752","title":"Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability","created":"2021-11-21","cited_by_count":84,"area":null},{"id":"2111.01124","title":"When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning?","created":"2021-11-01","cited_by_count":77,"area":null},{"id":"2203.05151","title":"Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity","created":"2022-03-10","cited_by_count":77,"area":null},{"id":"2112.12376","title":"Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization","created":"2021-12-23","cited_by_count":76,"area":null},{"id":"2106.04169","title":"On Improving Adversarial Transferability of Vision Transformers","created":"2021-06-08","cited_by_count":70,"area":null},{"id":"2303.14773","title":"BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning","created":"2023-03-26","cited_by_count":70,"area":null},{"id":"2107.13541","title":"Towards Robustness Against Natural Language Word Substitutions","created":"2021-07-28","cited_by_count":69,"area":null},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2108.09135","title":"PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifier","created":"2021-08-20","cited_by_count":65,"area":null},{"id":"2302.09491","title":"X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection","created":"2023-02-19","cited_by_count":59,"area":null},{"id":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57,"area":null},{"id":"2207.04718","title":"Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patches","created":"2022-07-11","cited_by_count":57,"area":null},{"id":"2206.04011","title":"Robust Semantic Communications with Masked VQ-VAE Enabled Codebook","created":"2022-06-08","cited_by_count":56,"area":null},{"id":"2103.10609","title":"Boosting Adversarial Transferability through Enhanced Momentum","created":"2021-03-19","cited_by_count":54,"area":null},{"id":"2302.09309","title":"StyleAdv: Meta Style Adversarial Training for Cross-Domain Few-Shot Learning","created":"2023-02-18","cited_by_count":53,"area":null},{"id":"2105.00203","title":"Adversarial Example Detection for DNN Models: A Review and Experimental Comparison","created":"2021-05-01","cited_by_count":52,"area":null},{"id":"2302.09923","title":"Prompt Stealing Attacks Against Text-to-Image Generation Models","created":"2023-02-20","cited_by_count":51,"area":null},{"id":"2112.11668","title":"How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?","created":"2021-12-22","cited_by_count":49,"area":null},{"id":"2203.08392","title":"Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?","created":"2022-03-16","cited_by_count":49,"area":null},{"id":"2202.05338","title":"Accountability in an Algorithmic Society: Relationality, Responsibility, and Robustness in Machine Learning","created":"2022-02-10","cited_by_count":48,"area":null},{"id":"2203.08008","title":"Beyond Explaining: Opportunities and Challenges of XAI-Based Model Improvement","created":"2022-03-15","cited_by_count":48,"area":null},{"id":"2306.06123","title":"Adversarial attacks and defenses in explainable artificial intelligence: A survey","created":"2023-06-06","cited_by_count":48,"area":null},{"id":"2103.14222","title":"Adversarial Attacks are Reversible with Natural Supervision","created":"2021-03-26","cited_by_count":47,"area":null},{"id":"2101.06784","title":"Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Driving","created":"2021-01-17","cited_by_count":45,"area":null},{"id":"2101.08030","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","created":"2021-01-20","cited_by_count":45,"area":null},{"id":"2504.10374","title":"Ctrl-Z: Controlling AI Agents via Resampling","created":"2025-04-14","cited_by_count":44,"area":null},{"id":"2106.03734","title":"Reveal of Vision Transformers Robustness against Adversarial Attacks","created":"2021-06-07","cited_by_count":43,"area":null},{"id":"2112.04764","title":"3D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detection","created":"2021-12-09","cited_by_count":43,"area":null},{"id":"2103.08951","title":"Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties","created":"2021-03-16","cited_by_count":41,"area":null},{"id":"2307.01778","title":"Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling","created":"2023-07-04","cited_by_count":41,"area":null},{"id":"2308.00788","title":"An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning","created":"2023-08-01","cited_by_count":41,"area":null},{"id":"2103.03344","title":"WaveGuard: Understanding and Mitigating Audio Adversarial Examples","created":"2021-03-04","cited_by_count":40,"area":null},{"id":"2305.11618","title":"DAP: A Dynamic Adversarial Patch for Evading Person Detectors","created":"2023-05-19","cited_by_count":40,"area":null},{"id":"2111.10659","title":"Are Vision Transformers Robust to Patch Perturbations?","created":"2021-11-20","cited_by_count":39,"area":null},{"id":"2203.14195","title":"How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective","created":"2022-03-27","cited_by_count":39,"area":null},{"id":"2104.00671","title":"TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothness","created":"2021-04-01","cited_by_count":38,"area":null},{"id":"2107.10137","title":"Improved Text Classification via Contrastive Adversarial Training","created":"2021-07-21","cited_by_count":38,"area":null},{"id":"2108.01220","title":"OVERT: An Algorithm for Safety Verification of Neural Network Control Policies for Nonlinear Systems","created":"2021-08-03","cited_by_count":38,"area":null},{"id":"2109.11926","title":"Sinkhorn Distributionally Robust Optimization","created":"2021-09-24","cited_by_count":38,"area":null},{"id":"2101.09126","title":"Will Artificial Intelligence supersede Earth System and Climate Models?","created":"2021-01-22","cited_by_count":37,"area":null},{"id":"2105.00529","title":"GRNN: Generative Regression Neural Network -- A Data Leakage Attack for Federated Learning","created":"2021-05-02","cited_by_count":36,"area":null},{"id":"2107.10171","title":"Leave-one-out Unfairness","created":"2021-07-21","cited_by_count":36,"area":null},{"id":"2111.10990","title":"Imperceptible Transfer Attack and Defense on 3D Point Cloud Classification","created":"2021-11-22","cited_by_count":36,"area":null},{"id":"2108.11765","title":"Physical Adversarial Attacks on an Aerial Imagery Object Detector","created":"2021-08-26","cited_by_count":35,"area":null},{"id":"2205.13152","title":"Transferable Adversarial Attack based on Integrated Gradients","created":"2022-05-26","cited_by_count":35,"area":null},{"id":"2304.00202","title":"Improving Fast Adversarial Training with Prior-Guided Knowledge","created":"2023-04-01","cited_by_count":35,"area":null},{"id":"2102.07325","title":"Cross-modal Adversarial Reprogramming","created":"2021-02-15","cited_by_count":34,"area":null},{"id":"2103.16241","title":"Improving robustness against common corruptions with frequency biased models","created":"2021-03-30","cited_by_count":34,"area":null},{"id":"2106.02105","title":"A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks","created":"2021-06-03","cited_by_count":34,"area":null},{"id":"2502.13175","title":"Towards Robust and Secure Embodied AI: A Survey on Vulnerabilities and Attacks","created":"2025-02-18","cited_by_count":34,"area":null},{"id":"2209.03463","title":"Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots","created":"2022-09-07","cited_by_count":33,"area":null},{"id":"2106.04938","title":"Attacking Adversarial Attacks as A Defense","created":"2021-06-09","cited_by_count":32,"area":null},{"id":"2201.07513","title":"Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders","created":"2022-01-19","cited_by_count":31,"area":null},{"id":"2103.04302","title":"Detecting Adversarial Examples from Sensitivity Inconsistency of Spatial-Transform Domain","created":"2021-03-07","cited_by_count":29,"area":null},{"id":"2110.14068","title":"Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks","created":"2021-10-26","cited_by_count":29,"area":null},{"id":"2111.09961","title":"A Review of Adversarial Attack and Defense for Classification Methods","created":"2021-11-18","cited_by_count":29,"area":null},{"id":"2108.01289","title":"AdvRush: Searching for Adversarially Robust Neural Architectures","created":"2021-08-03","cited_by_count":28,"area":null},{"id":"2110.15317","title":"Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework","created":"2021-10-28","cited_by_count":28,"area":null},{"id":"2104.12378","title":"Delving into Data: Effectively Substitute Training for Black-box Attack","created":"2021-04-26","cited_by_count":27,"area":null},{"id":"2201.06192","title":"Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systems","created":"2022-01-17","cited_by_count":27,"area":null},{"id":"2208.11180","title":"Auditing Membership Leakages of Multi-Exit Networks","created":"2022-08-23","cited_by_count":27,"area":null},{"id":"2306.12685","title":"Rethinking the Backward Propagation for Adversarial Transferability","created":"2023-06-22","cited_by_count":27,"area":null},{"id":"2103.12535","title":"NNrepair: Constraint-based Repair of Neural Network Classifiers","created":"2021-03-23","cited_by_count":26,"area":null},{"id":"2108.00146","title":"T$_k$ML-AP: Adversarial Attacks to Top-$k$ Multi-Label Learning","created":"2021-07-31","cited_by_count":26,"area":null},{"id":"2111.12485","title":"Understanding the Dynamics of DNNs Using Graph Modularity","created":"2021-11-24","cited_by_count":26,"area":null},{"id":"2106.05036","title":"Towards Defending against Adversarial Examples via Attack-Invariant Features","created":"2021-06-09","cited_by_count":25,"area":null},{"id":"2111.06628","title":"Learning to Break Deep Perceptual Hashing: The Use Case NeuralHash","created":"2021-11-12","cited_by_count":25,"area":null},{"id":"2209.14262","title":"A Survey on Physical Adversarial Attack in Computer Vision","created":"2022-09-28","cited_by_count":25,"area":null},{"id":"2210.08371","title":"Sketching for First Order Method: Efficient Algorithm for Low-Bandwidth Channel and Vulnerability","created":"2022-10-15","cited_by_count":25,"area":null},{"id":"2103.05347","title":"Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack","created":"2021-03-09","cited_by_count":24,"area":null},{"id":"2112.12982","title":"Parameter identifiability of a deep feedforward ReLU neural network","created":"2021-12-24","cited_by_count":24,"area":null},{"id":"2205.12331","title":"Certified Robustness Against Natural Language Attacks by Causal Intervention","created":"2022-05-24","cited_by_count":24,"area":null},{"id":"2104.02000","title":"Can audio-visual integration strengthen robustness under multimodal attacks?","created":"2021-04-05","cited_by_count":23,"area":null},{"id":"2503.03613","title":"CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIP","created":"2025-03-05","cited_by_count":23,"area":null},{"id":"2202.01811","title":"ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Masking","created":"2022-02-03","cited_by_count":22,"area":null},{"id":"2308.06015","title":"Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation","created":"2023-08-11","cited_by_count":22,"area":null},{"id":"2309.10544","title":"Model Leeching: An Extraction Attack Targeting LLMs","created":"2023-09-19","cited_by_count":22,"area":null},{"id":"2204.07932","title":"Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learning","created":"2022-04-17","cited_by_count":21,"area":null},{"id":"2212.09254","title":"TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization","created":"2022-12-19","cited_by_count":21,"area":null},{"id":"2301.01197","title":"Backdoor Attacks Against Dataset Distillation","created":"2023-01-03","cited_by_count":21,"area":null},{"id":"2202.02236","title":"Pixle: a fast and effective black-box attack based on rearranging pixels","created":"2022-02-04","cited_by_count":20,"area":null},{"id":"2203.16000","title":"StyleFool: Fooling Video Classification Systems via Style Transfer","created":"2022-03-30","cited_by_count":20,"area":null},{"id":"2302.09457","title":"Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective","created":"2023-02-19","cited_by_count":20,"area":null},{"id":"2103.14577","title":"Unsupervised Robust Domain Adaptation without Source Data","created":"2021-03-26","cited_by_count":19,"area":null},{"id":"2207.00425","title":"Transferable Graph Backdoor Attack","created":"2022-06-21","cited_by_count":19,"area":null},{"id":"2307.05772","title":"Random-Set Neural Networks (RS-NN)","created":"2023-07-11","cited_by_count":19,"area":null},{"id":"2310.00633","title":"A Survey of Robustness and Safety of 2D and 3D Deep Learning Models Against Adversarial Attacks","created":"2023-10-01","cited_by_count":19,"area":null},{"id":"2103.05292","title":"Deep Learning for Android Malware Defenses: a Systematic Literature Review","created":"2021-03-09","cited_by_count":18,"area":null},{"id":"2106.15202","title":"Inconspicuous Adversarial Patches for Fooling Image Recognition Systems on Mobile Devices","created":"2021-06-29","cited_by_count":18,"area":null},{"id":"2107.01396","title":"Demiguise Attack: Crafting Invisible Semantic Adversarial Perturbations with Perceptual Similarity","created":"2021-07-03","cited_by_count":18,"area":null},{"id":"2109.05463","title":"Logic Traps in Evaluating Attribution Scores","created":"2021-09-12","cited_by_count":18,"area":null},{"id":"2112.13178","title":"Gradient Leakage Attack Resilient Deep Learning","created":"2021-12-25","cited_by_count":18,"area":null}],"next_cursor":"100"}