{"total":26,"rows":[{"id":"2106.15023","title":"Evading Adversarial Example Detection Defenses with Orthogonal Projected Gradient Descent","created":"2021-06-28","cited_by_count":35,"area":null},{"id":"2203.01177","title":"Detecting Adversarial Perturbations in Multi-Task Perception","created":"2022-03-02","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":"2201.09650","title":"What You See is Not What the Network Infers: Detecting Adversarial Examples Based on Semantic Contradiction","created":"2022-01-24","cited_by_count":11,"area":null},{"id":"2311.17400","title":"Improving the Robustness of Transformer-based Large Language Models with Dynamic Attention","created":"2023-11-29","cited_by_count":10,"area":null},{"id":"2105.14676","title":"NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels","created":"2021-05-31","cited_by_count":8,"area":null},{"id":"2105.03689","title":"Self-Supervised Adversarial Example Detection by Disentangled Representation","created":"2021-05-08","cited_by_count":7,"area":null},{"id":"2209.00005","title":"Be Your Own Neighborhood: Detecting Adversarial Example by the Neighborhood Relations Built on Self-Supervised Learning","created":"2022-08-31","cited_by_count":5,"area":null},{"id":"2304.11359","title":"Detecting Adversarial Faces Using Only Real Face Self-Perturbations","created":"2023-04-22","cited_by_count":4,"area":null},{"id":"2404.12120","title":"Fortify the Guardian, Not the Treasure: Resilient Adversarial Detectors","created":"2024-04-18","cited_by_count":4,"area":null},{"id":"2105.10101","title":"Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks","created":"2021-05-21","cited_by_count":3,"area":null},{"id":"2208.09195","title":"Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks","created":"2022-08-19","cited_by_count":3,"area":null},{"id":"2408.10647","title":"Privacy-preserving Universal Adversarial Defense for Black-box Models","created":"2024-08-20","cited_by_count":3,"area":null},{"id":"2406.03017","title":"DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domain","created":"2024-06-05","cited_by_count":2,"area":null},{"id":"2107.07043","title":"GGT: Graph-Guided Testing for Adversarial Sample Detection of Deep Neural Network","created":"2021-07-09","cited_by_count":1,"area":null},{"id":"2305.04436","title":"Adversarial Examples Detection with Enhanced Image Difference Features based on Local Histogram Equalization","created":"2023-05-08","cited_by_count":1,"area":null},{"id":"2306.02021","title":"Towards Black-box Adversarial Example Detection: A Data Reconstruction-based Method","created":"2023-06-03","cited_by_count":1,"area":null},{"id":"2406.13920","title":"Understanding the Robustness of Graph Neural Networks against Adversarial Attacks","created":"2024-06-20","cited_by_count":1,"area":null},{"id":"2505.12586","title":"A Few Large Shifts: Layer-Inconsistency Based Minimal Overhead Adversarial Example Detection","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2206.13083","title":"Adversarial Example Detection in Deployed Tree Ensembles","created":"2022-06-27","cited_by_count":0,"area":null},{"id":"2404.12635","title":"AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation","created":"2024-04-19","cited_by_count":0,"area":null},{"id":"2409.19619","title":"Discerning the Chaos: Detecting Adversarial Perturbations while Disentangling Intentional from Unintentional Noises","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2411.06666","title":"Adversarial Detection with a Dynamically Stable System","created":"2024-11-11","cited_by_count":0,"area":null},{"id":"2512.03625","title":"FeatureLens: A Highly Generalizable and Interpretable Framework for Detecting Adversarial Examples Based on Image Features","created":"2025-12-03","cited_by_count":0,"area":null},{"id":"2606.01560","title":"GJDNet: Robust Graph Neural Networks via Joint Disentangled Learning Against Adversarial Attacks","created":"2026-06-01","cited_by_count":0,"area":null},{"id":"2608.07750","title":"Multi-Task Consistency-based Detection of Adversarial Attacks","created":"2026-08-07","cited_by_count":0,"area":null}],"next_cursor":null}