{"total":21,"rows":[{"id":"2307.10562","title":"Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples","created":"2023-07-20","cited_by_count":43,"area":null},{"id":"2405.16134","title":"Breaking the False Sense of Security in Backdoor Defense through Re-Activation Attack","created":"2024-05-25","cited_by_count":28,"area":null},{"id":"2502.18508","title":"REFINE: Inversion-Free Backdoor Defense via Model Reprogramming","created":"2025-02-22","cited_by_count":22,"area":null},{"id":"2407.19845","title":"BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning","created":"2024-07-29","cited_by_count":19,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2405.16112","title":"Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor","created":"2024-05-25","cited_by_count":11,"area":null},{"id":"2405.19928","title":"BAN: Detecting Backdoors Activated by Adversarial Neuron Noise","created":"2024-05-30","cited_by_count":8,"area":null},{"id":"2510.16171","title":"Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness","created":"2025-10-17","cited_by_count":8,"area":null},{"id":"2407.06838","title":"Event Trojan: Asynchronous Event-based Backdoor Attacks","created":"2024-07-09","cited_by_count":3,"area":null},{"id":"2411.11525","title":"Reliable Poisoned Sample Detection against Backdoor Attacks Enhanced by Sharpness Aware Minimization","created":"2024-11-18","cited_by_count":3,"area":null},{"id":"2502.11687","title":"ReVeil: Unconstrained Concealed Backdoor Attack on Deep Neural Networks using Machine Unlearning","created":"2025-02-17","cited_by_count":3,"area":null},{"id":"2601.00968","title":"Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attacks","created":"2026-01-02","cited_by_count":3,"area":null},{"id":"2312.07955","title":"Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking","created":"2023-12-13","cited_by_count":2,"area":null},{"id":"2411.09776","title":"Combining Machine Learning Defenses without Conflicts","created":"2024-11-14","cited_by_count":2,"area":null},{"id":"2408.11679","title":"BadVim: Unveiling Backdoor Threats in Visual State Space Model","created":"2024-08-21","cited_by_count":1,"area":null},{"id":"2502.18520","title":"Class-Conditional Neural Polarizer: A Lightweight and Effective Backdoor Defense by Purifying Poisoned Features","created":"2025-02-23","cited_by_count":1,"area":null},{"id":"2509.03179","title":"AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain","created":"2025-09-03","cited_by_count":1,"area":null},{"id":"2602.10780","title":"Kill it with FIRE: On Leveraging Latent Space Directions for Runtime Backdoor Mitigation in Deep Neural Networks","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2401.15002","title":"BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning","created":"2024-01-26","cited_by_count":0,"area":null},{"id":"2403.02950","title":"A general approach to enhance the survivability of backdoor attacks by decision path coupling","created":"2024-03-05","cited_by_count":0,"area":null},{"id":"2610.06994","title":"TARE: Weigh a Never-Poisoned Twin Before Reading Backdoor-Defense Costs","created":"2026-10-04","cited_by_count":0,"area":null}],"next_cursor":null}