{"total":109,"rows":[{"id":"2205.07460","title":"Diffusion Models for Adversarial Purification","created":"2022-05-16","cited_by_count":476,"area":"diffusion"},{"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":"2202.10103","title":"Robustness and Accuracy Could Be Reconcilable by (Proper) Definition","created":"2022-02-21","cited_by_count":111,"area":null},{"id":"2109.05211","title":"RobustART: Benchmarking Robustness on Architecture Design and Training Techniques","created":"2021-09-11","cited_by_count":79,"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":"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":"2102.12827","title":"Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints","created":"2021-02-25","cited_by_count":50,"area":null},{"id":"2203.15859","title":"NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models","created":"2022-03-29","cited_by_count":50,"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":"2106.06946","title":"Boosting Randomized Smoothing with Variance Reduced Classifiers","created":"2021-06-13","cited_by_count":47,"area":null},{"id":"2212.05630","title":"DISCO: Adversarial Defense with Local Implicit Functions","created":"2022-12-11","cited_by_count":34,"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":"2204.00491","title":"FrequencyLowCut Pooling -- Plug & Play against Catastrophic Overfitting","created":"2022-04-01","cited_by_count":28,"area":null},{"id":"2302.02213","title":"CosPGD: an efficient white-box adversarial attack for pixel-wise prediction tasks","created":"2023-02-04","cited_by_count":27,"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":"2210.05938","title":"Robust Models are less Over-Confident","created":"2022-10-12","cited_by_count":25,"area":null},{"id":"2101.03272","title":"Exploring Adversarial Fake Images on Face Manifold","created":"2021-01-09","cited_by_count":24,"area":null},{"id":"2106.09947","title":"Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples","created":"2021-06-18","cited_by_count":23,"area":null},{"id":"2206.07179","title":"Proximal Splitting Adversarial Attacks for Semantic Segmentation","created":"2022-06-14","cited_by_count":23,"area":null},{"id":"2105.08714","title":"Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks","created":"2021-05-18","cited_by_count":20,"area":null},{"id":"2203.15331","title":"CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters","created":"2022-03-29","cited_by_count":20,"area":null},{"id":"2101.04401","title":"Robustness of on-device Models: Adversarial Attack to Deep Learning Models on Android Apps","created":"2021-01-12","cited_by_count":19,"area":null},{"id":"2307.04333","title":"Enhancing Adversarial Robustness via Score-Based Optimization","created":"2023-07-10","cited_by_count":19,"area":"diffusion"},{"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":"2104.09197","title":"Removing Adversarial Noise in Class Activation Feature Space","created":"2021-04-19","cited_by_count":18,"area":null},{"id":"2312.04802","title":"MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model","created":"2023-12-08","cited_by_count":18,"area":"diffusion"},{"id":"2106.10606","title":"Attack to Fool and Explain Deep Networks","created":"2021-06-20","cited_by_count":17,"area":null},{"id":"2112.05005","title":"Mutual Adversarial Training: Learning together is better than going alone","created":"2021-12-09","cited_by_count":17,"area":null},{"id":"2103.02695","title":"Shift Invariance Can Reduce Adversarial Robustness","created":"2021-03-03","cited_by_count":16,"area":null},{"id":"2104.03356","title":"Universal Spectral Adversarial Attacks for Deformable Shapes","created":"2021-04-07","cited_by_count":14,"area":null},{"id":"2404.19460","title":"AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples","created":"2024-04-30","cited_by_count":14,"area":null},{"id":"2207.12203","title":"Improving Adversarial Robustness via Mutual Information Estimation","created":"2022-07-25","cited_by_count":13,"area":null},{"id":"2310.18762","title":"Purify++: Improving Diffusion-Purification with Advanced Diffusion Models and Control of Randomness","created":"2023-10-28","cited_by_count":13,"area":"diffusion"},{"id":"2204.00487","title":"Robust and Accurate -- Compositional Architectures for Randomized Smoothing","created":"2022-04-01","cited_by_count":12,"area":null},{"id":"2306.12941","title":"Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models","created":"2023-06-22","cited_by_count":12,"area":null},{"id":"2309.13609","title":"Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks","created":"2023-09-24","cited_by_count":12,"area":null},{"id":"2203.07341","title":"Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis","created":"2022-03-14","cited_by_count":10,"area":null},{"id":"2106.01538","title":"PDPGD: Primal-Dual Proximal Gradient Descent Adversarial Attack","created":"2021-06-03","cited_by_count":8,"area":null},{"id":"2109.09901","title":"Modeling Adversarial Noise for Adversarial Training","created":"2021-09-21","cited_by_count":8,"area":null},{"id":"2308.07834","title":"Simple and Efficient Partial Graph Adversarial Attack: A New Perspective","created":"2023-08-15","cited_by_count":8,"area":null},{"id":"2310.06227","title":"Exploring adversarial attacks in federated learning for medical imaging","created":"2023-10-10","cited_by_count":8,"area":null},{"id":"2102.07140","title":"Perceptually Constrained Adversarial Attacks","created":"2021-02-14","cited_by_count":7,"area":null},{"id":"2104.00322","title":"Domain Invariant Adversarial Learning","created":"2021-04-01","cited_by_count":7,"area":null},{"id":"2104.11103","title":"Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions","created":"2021-04-22","cited_by_count":7,"area":null},{"id":"2111.01714","title":"Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks","created":"2021-11-02","cited_by_count":7,"area":null},{"id":"2210.00973","title":"NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning","created":"2022-10-03","cited_by_count":7,"area":null},{"id":"2402.01879","title":"$\\sigma$-zero: Gradient-based Optimization of $\\ell_0$-norm Adversarial Examples","created":"2024-02-02","cited_by_count":7,"area":null},{"id":"2102.03156","title":"Optimal Transport as a Defense Against Adversarial Attacks","created":"2021-02-05","cited_by_count":6,"area":null},{"id":"2201.01235","title":"On the Minimal Adversarial Perturbation for Deep Neural Networks with Provable Estimation Error","created":"2022-01-04","cited_by_count":6,"area":null},{"id":"2204.12050","title":"Self-recoverable Adversarial Examples: A New Effective Protection Mechanism in Social Networks","created":"2022-04-26","cited_by_count":6,"area":null},{"id":"2211.12713","title":"Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles","created":"2022-11-23","cited_by_count":6,"area":null},{"id":"2303.13401","title":"Optimization and Optimizers for Adversarial Robustness","created":"2023-03-23","cited_by_count":6,"area":null},{"id":"2304.06919","title":"Interpretability is a Kind of Safety: An Interpreter-based Ensemble for Adversary Defense","created":"2023-04-14","cited_by_count":6,"area":null},{"id":"2403.02723","title":"Minimum Topology Attacks for Graph Neural Networks","created":"2024-03-05","cited_by_count":6,"area":null},{"id":"2403.11448","title":"Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM","created":"2024-03-18","cited_by_count":6,"area":null},{"id":"2102.11586","title":"Adversarial Examples Detection beyond Image Space","created":"2021-02-23","cited_by_count":5,"area":null},{"id":"2308.05232","title":"SegMatch: A semi-supervised learning method for surgical instrument segmentation","created":"2023-08-09","cited_by_count":5,"area":null},{"id":"2308.07673","title":"A Review of Adversarial Attacks in Computer Vision","created":"2023-08-15","cited_by_count":5,"area":null},{"id":"2310.02480","title":"Splitting the Difference on Adversarial Training","created":"2023-10-03","cited_by_count":5,"area":null},{"id":"2404.03340","title":"Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks","created":"2024-04-04","cited_by_count":5,"area":null},{"id":"2408.05900","title":"Classifier Guidance Enhances Diffusion-based Adversarial Purification by Preserving Predictive Information","created":"2024-08-12","cited_by_count":5,"area":"diffusion"},{"id":"2203.04886","title":"Reverse Engineering $\\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees","created":"2022-03-09","cited_by_count":4,"area":null},{"id":"2206.07842","title":"Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning","created":"2022-06-15","cited_by_count":4,"area":null},{"id":"2209.04293","title":"Robust-by-Design Classification via Unitary-Gradient Neural Networks","created":"2022-09-09","cited_by_count":4,"area":null},{"id":"2310.12708","title":"Generating Robust Adversarial Examples against Online Social Networks (OSNs)","created":"2023-10-19","cited_by_count":4,"area":null},{"id":"2310.12793","title":"OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift","created":"2023-10-19","cited_by_count":4,"area":null},{"id":"2103.15385","title":"Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training","created":"2021-03-29","cited_by_count":3,"area":null},{"id":"2108.00180","title":"Delving into Deep Image Prior for Adversarial Defense: A Novel Reconstruction-based Defense Framework","created":"2021-07-31","cited_by_count":3,"area":null},{"id":"2108.00335","title":"Towards Adversarially Robust and Domain Generalizable Stereo Matching by Rethinking DNN Feature Backbones","created":"2021-07-31","cited_by_count":3,"area":null},{"id":"2111.15603","title":"Human Imperceptible Attacks and Applications to Improve Fairness","created":"2021-11-30","cited_by_count":3,"area":null},{"id":"2202.07453","title":"Random Walks for Adversarial Meshes","created":"2022-02-15","cited_by_count":3,"area":null},{"id":"2305.04067","title":"The Best Defense is Attack: Repairing Semantics in Textual Adversarial Examples","created":"2023-05-06","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":"2407.03946","title":"TrackPGD: Efficient Adversarial Attack using Object Binary Masks against Robust Transformer Trackers","created":"2024-07-04","cited_by_count":3,"area":null},{"id":"2511.11009","title":"Unsupervised Robust Domain Adaptation: Paradigm, Theory and Algorithm","created":"2025-11-14","cited_by_count":3,"area":null},{"id":"2212.07992","title":"Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks","created":"2022-12-15","cited_by_count":2,"area":null},{"id":"2304.09875","title":"GREAT Score: Global Robustness Evaluation of Adversarial Perturbation using Generative Models","created":"2023-04-19","cited_by_count":2,"area":null},{"id":"2305.07308","title":"Efficient Search of Comprehensively Robust Neural Architectures via Multi-fidelity Evaluation","created":"2023-05-12","cited_by_count":2,"area":null},{"id":"2406.10933","title":"Improving Adversarial Robustness via Decoupled Visual Representation Masking","created":"2024-06-16","cited_by_count":2,"area":null},{"id":"2507.03450","title":"Evaluating the Evaluators: Trust in Adversarial Robustness Tests","created":"2025-07-04","cited_by_count":2,"area":null},{"id":"2510.21532","title":"FrameShield: Adversarially Robust Video Anomaly Detection","created":"2025-10-24","cited_by_count":2,"area":null},{"id":"2111.10844","title":"Denoised Internal Models: a Brain-Inspired Autoencoder against Adversarial Attacks","created":"2021-11-21","cited_by_count":1,"area":null},{"id":"2201.09369","title":"Efficient and Robust Classification for Sparse Attacks","created":"2022-01-23","cited_by_count":1,"area":null},{"id":"2202.08892","title":"Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies","created":"2022-02-17","cited_by_count":1,"area":null},{"id":"2303.12481","title":"Revisiting DeepFool: generalization and improvement","created":"2023-03-22","cited_by_count":1,"area":null},{"id":"2406.16501","title":"UNICAD: A Unified Approach for Attack Detection, Noise Reduction and Novel Class Identification","created":"2024-06-24","cited_by_count":1,"area":null},{"id":"2410.16579","title":"Conflict-Aware Adversarial Training","created":"2024-10-21","cited_by_count":1,"area":null},{"id":"2410.20432","title":"Integrating uncertainty quantification into randomized smoothing based robustness guarantees","created":"2024-10-27","cited_by_count":1,"area":null},{"id":"2501.13336","title":"Gradient-Free Adversarial Purification with Diffusion Models","created":"2025-01-23","cited_by_count":1,"area":"diffusion"},{"id":"2507.22428","title":"Theoretical Analysis of Relative Errors in Gradient Computations for Adversarial Attacks with CE Loss","created":"2025-07-30","cited_by_count":1,"area":null},{"id":"2605.27135","title":"Do Modern Post-Hoc Watermarking Methods Beat Broken-Arrows?","created":"2026-05-26","cited_by_count":1,"area":null},{"id":"2105.00278","title":"A Perceptual Distortion Reduction Framework: Towards Generating Adversarial Examples with High Perceptual Quality and Attack Success Rate","created":"2021-05-01","cited_by_count":0,"area":null},{"id":"2105.14710","title":"Robustifying $\\ell_\\infty$ Adversarial Training to the Union of Perturbation Models","created":"2021-05-31","cited_by_count":0,"area":null},{"id":"2112.01777","title":"Attack-Centric Approach for Evaluating Transferability of Adversarial Samples in Machine Learning Models","created":"2021-12-03","cited_by_count":0,"area":null},{"id":"2201.08731","title":"Low-Interception Waveform: To Prevent the Recognition of Spectrum Waveform Modulation via Adversarial Examples","created":"2022-01-20","cited_by_count":0,"area":null},{"id":"2208.06984","title":"A Multi-objective Memetic Algorithm for Auto Adversarial Attack Optimization Design","created":"2022-08-15","cited_by_count":0,"area":null},{"id":"2303.11793","title":"Bridging Optimal Transport and Jacobian Regularization by Optimal Trajectory for Enhanced Adversarial Defense","created":"2023-03-21","cited_by_count":0,"area":null},{"id":"2307.09804","title":"Fix your downsampling ASAP! Be natively more robust via Aliasing and Spectral Artifact free Pooling","created":"2023-07-19","cited_by_count":0,"area":null},{"id":"2401.12014","title":"Robustness to distribution shifts of compressed networks for edge devices","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2401.13212","title":"AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation","created":"2024-01-24","cited_by_count":0,"area":null}],"next_cursor":"100"}