{"total":27,"rows":[{"id":"2202.06382","title":"Training with More Confidence: Mitigating Injected and Natural Backdoors During Training","created":"2022-02-13","cited_by_count":38,"area":null},{"id":"2201.13178","title":"Few-Shot Backdoor Attacks on Visual Object Tracking","created":"2022-01-31","cited_by_count":35,"area":null},{"id":"2408.07362","title":"BadMerging: Backdoor Attacks Against Model Merging","created":"2024-08-14","cited_by_count":34,"area":null},{"id":"2203.00858","title":"Physical Backdoor Attacks to Lane Detection Systems in Autonomous Driving","created":"2022-03-02","cited_by_count":31,"area":null},{"id":"2312.12585","title":"BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning","created":"2023-12-19","cited_by_count":30,"area":null},{"id":"2205.03190","title":"Imperceptible Backdoor Attack: From Input Space to Feature Representation","created":"2022-05-06","cited_by_count":27,"area":null},{"id":"2202.03609","title":"PolicyCleanse: Backdoor Detection and Mitigation in Reinforcement Learning","created":"2022-02-08","cited_by_count":19,"area":null},{"id":"2211.10530","title":"Provable Defense against Backdoor Policies in Reinforcement Learning","created":"2022-11-18","cited_by_count":18,"area":null},{"id":"2302.03322","title":"Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence","created":"2023-02-07","cited_by_count":17,"area":null},{"id":"2205.02589","title":"A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning","created":"2022-05-05","cited_by_count":16,"area":null},{"id":"2211.11030","title":"Adversarial Cheap Talk","created":"2022-11-20","cited_by_count":16,"area":null},{"id":"2302.13851","title":"Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks","created":"2023-02-27","cited_by_count":14,"area":null},{"id":"2307.07670","title":"Efficient Adversarial Attacks on Online Multi-agent Reinforcement Learning","created":"2023-07-15","cited_by_count":10,"area":null},{"id":"2402.03741","title":"SUB-PLAY: Adversarial Policies against Partially Observed Multi-Agent Reinforcement Learning Systems","created":"2024-02-06","cited_by_count":7,"area":null},{"id":"2412.06219","title":"Data Free Backdoor Attacks","created":"2024-12-09","cited_by_count":7,"area":null},{"id":"2305.10091","title":"Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challenges","created":"2023-05-17","cited_by_count":5,"area":null},{"id":"2206.09272","title":"DECK: Model Hardening for Defending Pervasive Backdoors","created":"2022-06-18","cited_by_count":4,"area":null},{"id":"2211.10933","title":"Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification","created":"2022-11-20","cited_by_count":4,"area":null},{"id":"2210.11237","title":"Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey","created":"2022-10-19","cited_by_count":3,"area":null},{"id":"2411.16512","title":"Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models","created":"2024-11-25","cited_by_count":3,"area":null},{"id":"2305.16621","title":"A Reminder of its Brittleness: Language Reward Shaping May Hinder Learning for Instruction Following Agents","created":"2023-05-26","cited_by_count":2,"area":null},{"id":"2505.22829","title":"Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies","created":"2025-05-28","cited_by_count":2,"area":null},{"id":"2311.00859","title":"Optimal Cost Constrained Adversarial Attacks For Multiple Agent Systems","created":"2023-11-01","cited_by_count":1,"area":null},{"id":"2410.04823","title":"CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2503.20925","title":"Prototype Guided Backdoor Defense","created":"2025-03-26","cited_by_count":0,"area":null},{"id":"2605.21146","title":"Detecting Trojaned DNNs via Spectral Regression Analysis","created":"2026-05-20","cited_by_count":0,"area":null},{"id":"2606.00414","title":"Auditing Near-Optimal Policies Can Be Exponentially Hard: Conditional Query Lower Bounds via Occupancy Rashomon Capacity","created":"2026-05-29","cited_by_count":0,"area":null}],"next_cursor":null}