{"total":71,"rows":[{"id":"2108.09034","title":"AdvDrop: Adversarial Attack to DNNs by Dropping Information","created":"2021-08-20","cited_by_count":56,"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":"2212.05630","title":"DISCO: Adversarial Defense with Local Implicit Functions","created":"2022-12-11","cited_by_count":34,"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":"2307.07653","title":"RFLA: A Stealthy Reflected Light Adversarial Attack in the Physical World","created":"2023-07-14","cited_by_count":23,"area":null},{"id":"2205.12134","title":"Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks","created":"2022-05-24","cited_by_count":19,"area":null},{"id":"2103.14211","title":"MagDR: Mask-guided Detection and Reconstruction for Defending Deepfakes","created":"2021-03-26","cited_by_count":17,"area":null},{"id":"2106.10606","title":"Attack to Fool and Explain Deep Networks","created":"2021-06-20","cited_by_count":17,"area":null},{"id":"2112.06116","title":"Stereoscopic Universal Perturbations across Different Architectures and Datasets","created":"2021-12-12","cited_by_count":17,"area":null},{"id":"2101.11466","title":"Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting","created":"2021-01-27","cited_by_count":16,"area":null},{"id":"2211.09565","title":"Towards Good Practices in Evaluating Transfer Adversarial Attacks","created":"2022-11-17","cited_by_count":15,"area":null},{"id":"2101.01121","title":"Local Competition and Stochasticity for Adversarial Robustness in Deep Learning","created":"2021-01-04","cited_by_count":12,"area":null},{"id":"2205.14283","title":"Rethinking Bayesian Learning for Data Analysis: The Art of Prior and Inference in Sparsity-Aware Modeling","created":"2022-05-28","cited_by_count":12,"area":null},{"id":"2210.00430","title":"Understanding Adversarial Robustness Against On-manifold Adversarial Examples","created":"2022-10-02","cited_by_count":12,"area":null},{"id":"2310.07492","title":"Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models","created":"2023-10-11","cited_by_count":12,"area":"diffusion"},{"id":"2208.00428","title":"Robust Real-World Image Super-Resolution against Adversarial Attacks","created":"2022-07-31","cited_by_count":11,"area":null},{"id":"2510.24195","title":"Vanish into Thin Air: Cross-prompt Universal Adversarial Attacks for SAM2","created":"2025-10-28","cited_by_count":11,"area":null},{"id":"2203.01439","title":"Enhancing Adversarial Robustness for Deep Metric Learning","created":"2022-03-02","cited_by_count":10,"area":null},{"id":"2504.14541","title":"Towards Model Resistant to Transferable Adversarial Examples via Trigger Activation","created":"2025-04-20","cited_by_count":10,"area":null},{"id":"2103.05354","title":"Revisiting Model's Uncertainty and Confidences for Adversarial Example Detection","created":"2021-03-09","cited_by_count":9,"area":null},{"id":"2310.03967","title":"Sub-token ViT Embedding via Stochastic Resonance Transformers","created":"2023-10-06","cited_by_count":9,"area":null},{"id":"2106.03614","title":"Adversarial Attack and Defense in Deep Ranking","created":"2021-06-07","cited_by_count":8,"area":null},{"id":"2402.08225","title":"Improving Black-box Robustness with In-Context Rewriting","created":"2024-02-13","cited_by_count":8,"area":null},{"id":"2206.13607","title":"Improved Text Classification via Test-Time Augmentation","created":"2022-06-27","cited_by_count":7,"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":"2205.07874","title":"How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation","created":"2022-05-13","cited_by_count":6,"area":null},{"id":"2210.09588","title":"Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks","created":"2022-10-18","cited_by_count":6,"area":null},{"id":"2307.07269","title":"Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentation","created":"2023-07-14","cited_by_count":6,"area":null},{"id":"2410.03376","title":"Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization","created":"2024-10-04","cited_by_count":6,"area":"deeprl"},{"id":"2112.09219","title":"All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines","created":"2021-12-16","cited_by_count":5,"area":null},{"id":"2110.10444","title":"Moir\\'e Attack (MA): A New Potential Risk of Screen Photos","created":"2021-10-20","cited_by_count":4,"area":null},{"id":"2212.01957","title":"CSTAR: Towards Compact and STructured Deep Neural Networks with Adversarial Robustness","created":"2022-12-04","cited_by_count":4,"area":null},{"id":"2212.05387","title":"General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignments","created":"2022-12-11","cited_by_count":4,"area":null},{"id":"2508.05989","title":"ETA: Energy-based Test-time Adaptation for Depth Completion","created":"2025-08-08","cited_by_count":4,"area":null},{"id":"2203.15529","title":"Treatment Learning Causal Transformer for Noisy Image Classification","created":"2022-03-29","cited_by_count":3,"area":null},{"id":"2305.15709","title":"PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation","created":"2023-05-25","cited_by_count":3,"area":null},{"id":"2402.18787","title":"Enhancing the \"Immunity\" of Mixture-of-Experts Networks for Adversarial Defense","created":"2024-02-29","cited_by_count":3,"area":"moe"},{"id":"2404.08069","title":"Persistent Classification: A New Approach to Stability of Data and Adversarial Examples","created":"2024-04-11","cited_by_count":3,"area":null},{"id":"2105.08269","title":"Sparta: Spatially Attentive and Adversarially Robust Activation","created":"2021-05-18","cited_by_count":2,"area":null},{"id":"2106.02867","title":"Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness","created":"2021-06-05","cited_by_count":2,"area":null},{"id":"2112.14340","title":"Super-Efficient Super Resolution for Fast Adversarial Defense at the Edge","created":"2021-12-29","cited_by_count":2,"area":null},{"id":"2211.01579","title":"Data-free Defense of Black Box Models Against Adversarial Attacks","created":"2022-11-03","cited_by_count":2,"area":null},{"id":"2410.17442","title":"Detecting Adversarial Examples","created":"2024-10-22","cited_by_count":2,"area":null},{"id":"2411.00839","title":"CausAdv: A Causal-based Framework for Detecting Adversarial Examples","created":"2024-10-29","cited_by_count":2,"area":null},{"id":"2506.20816","title":"Universal and Efficient Detection of Adversarial Data through Nonuniform Impact on Network Layers","created":"2025-06-25","cited_by_count":2,"area":null},{"id":"2104.02155","title":"Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification","created":"2021-04-05","cited_by_count":1,"area":null},{"id":"2104.11977","title":"Generalized moduli of continuity under irregular or random deformations via multiscale analysis","created":"2021-04-24","cited_by_count":1,"area":null},{"id":"2112.09279","title":"Robust Upper Bounds for Adversarial Training","created":"2021-12-17","cited_by_count":1,"area":null},{"id":"2203.00302","title":"Adversarial samples for deep monocular 6D object pose estimation","created":"2022-03-01","cited_by_count":1,"area":null},{"id":"2410.24214","title":"ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs","created":"2024-10-31","cited_by_count":1,"area":null},{"id":"2502.10452","title":"Quaternion-Hadamard Network: A Novel Defense Against Adversarial Attacks with a New Dataset","created":"2025-02-12","cited_by_count":1,"area":null},{"id":"2510.27245","title":"Trans-defense: Transformer-based Denoiser for Adversarial Defense with Spatial-Frequency Domain Representation","created":"2025-10-31","cited_by_count":1,"area":null},{"id":"2103.04565","title":"Improving Transformation-based Defenses against Adversarial Examples with First-order Perturbations","created":"2021-03-08","cited_by_count":0,"area":null},{"id":"2105.06807","title":"Salient Feature Extractor for Adversarial Defense on Deep Neural Networks","created":"2021-05-14","cited_by_count":0,"area":null},{"id":"2107.12473","title":"Adversarial Attacks with Time-Scale Representations","created":"2021-07-26","cited_by_count":0,"area":null},{"id":"2110.07801","title":"Adversarial Purification through Representation Disentanglement","created":"2021-10-15","cited_by_count":0,"area":null},{"id":"2110.15829","title":"Holistic Deep Learning","created":"2021-10-29","cited_by_count":0,"area":null},{"id":"2304.04539","title":"UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for Classifying Common Mental Illnesses on Social Media Posts","created":"2023-04-10","cited_by_count":0,"area":null},{"id":"2311.15339","title":"Adversarial Purification of Information Masking","created":"2023-11-26","cited_by_count":0,"area":null},{"id":"2402.08035","title":"Multiple Random Masking Autoencoder Ensembles for Robust Multimodal Semi-supervised Learning","created":"2024-02-12","cited_by_count":0,"area":null},{"id":"2403.08170","title":"Versatile Defense Against Adversarial Attacks on Image Recognition","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2406.02253","title":"PuFace: Defending against Facial Cloaking Attacks for Facial Recognition Models","created":"2024-06-04","cited_by_count":0,"area":null},{"id":"2408.00023","title":"On the Perturbed States for Transformed Input-robust Reinforcement Learning","created":"2024-07-31","cited_by_count":0,"area":"deeprl"},{"id":"2501.01908","title":"Training-Free Adversarial Robustness in Computational MRI","created":"2025-01-03","cited_by_count":0,"area":null},{"id":"2504.01399","title":"Leveraging Generalizability of Image-to-Image Translation for Enhanced Adversarial Defense","created":"2025-04-02","cited_by_count":0,"area":null},{"id":"2506.18248","title":"Improving Black-Box Generative Attacks via Generator Semantic Consistency","created":"2025-06-23","cited_by_count":0,"area":null},{"id":"2509.22015","title":"Concept-SAE: A Controllable and Invertible Concept Interface for Sparse Autoencoders","created":"2025-09-26","cited_by_count":0,"area":"mechinterp"},{"id":"2605.02109","title":"Detecting Adversarial Data via Provable Adversarial Noise Amplification","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2606.31378","title":"MAPE: Defending Against Transferable Adversarial Attacks Using Multi-Source Adversarial Perturbations Elimination","created":"2026-06-30","cited_by_count":0,"area":null},{"id":"2609.08331","title":"Do Input-Level Defenses Transfer to Observation-Level Attacks on VideoLLMs?","created":"2026-09-08","cited_by_count":0,"area":"inference"},{"id":"2609.39265","title":"Universal Cross-Prompt Adversarial Attacks on Promptable Concept Segmentation","created":"2026-09-30","cited_by_count":0,"area":null}],"next_cursor":null}