{"total":61,"rows":[{"id":"2302.10149","title":"Poisoning Web-Scale Training Datasets is Practical","created":"2023-02-20","cited_by_count":290,"area":null},{"id":"2110.03735","title":"Adversarial Unlearning of Backdoors via Implicit Hypergradient","created":"2021-10-07","cited_by_count":166,"area":null},{"id":"2202.03423","title":"Backdoor Defense via Decoupling the Training Process","created":"2022-02-05","cited_by_count":153,"area":null},{"id":"2204.05255","title":"Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information","created":"2022-04-11","cited_by_count":103,"area":null},{"id":"2209.06015","title":"Black-box Dataset Ownership Verification via Backdoor Watermarking","created":"2022-08-04","cited_by_count":65,"area":null},{"id":"2310.14942","title":"Domain Watermark: Effective and Harmless Dataset Copyright Protection is Closed at Hand","created":"2023-10-09","cited_by_count":57,"area":null},{"id":"2112.03476","title":"Defending against Model Stealing via Verifying Embedded External Features","created":"2021-12-07","cited_by_count":46,"area":null},{"id":"2303.12175","title":"Black-box Backdoor Defense via Zero-shot Image Purification","created":"2023-03-21","cited_by_count":45,"area":null},{"id":"2303.12993","title":"Backdoor Defense via Adaptively Splitting Poisoned Dataset","created":"2023-03-23","cited_by_count":43,"area":null},{"id":"2111.08429","title":"An Overview of Backdoor Attacks Against Deep Neural Networks and Possible Defences","created":"2021-11-16","cited_by_count":42,"area":null},{"id":"2405.09786","title":"IBD-PSC: Input-level Backdoor Detection via Parameter-oriented Scaling Consistency","created":"2024-05-16","cited_by_count":39,"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":"2111.12965","title":"Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networks","created":"2021-11-25","cited_by_count":34,"area":null},{"id":"2302.01762","title":"BackdoorBox: A Python Toolbox for Backdoor Learning","created":"2023-02-01","cited_by_count":26,"area":null},{"id":"2201.08619","title":"Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World","created":"2022-01-21","cited_by_count":22,"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":"2104.07395","title":"Robust Backdoor Attacks against Deep Neural Networks in Real Physical World","created":"2021-04-15","cited_by_count":20,"area":null},{"id":"2306.06209","title":"Backdoor Attack with Sparse and Invisible Trigger","created":"2023-05-11","cited_by_count":20,"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":"2302.12758","title":"Defending Against Backdoor Attacks by Layer-wise Feature Analysis","created":"2023-02-24","cited_by_count":16,"area":null},{"id":"2409.13864","title":"Persistent Backdoor Attacks in Continual Learning","created":"2024-09-20","cited_by_count":13,"area":null},{"id":"2211.01806","title":"BATT: Backdoor Attack with Transformation-based Triggers","created":"2022-11-02","cited_by_count":11,"area":null},{"id":"2308.12439","title":"BaDExpert: Extracting Backdoor Functionality for Accurate Backdoor Input Detection","created":"2023-08-23","cited_by_count":11,"area":null},{"id":"2207.05641","title":"Backdoor Attacks on Crowd Counting","created":"2022-07-12","cited_by_count":10,"area":null},{"id":"2211.05631","title":"Backdoor Defense via Suppressing Model Shortcuts","created":"2022-11-02","cited_by_count":10,"area":null},{"id":"2306.00816","title":"Versatile Backdoor Attack with Visible, Semantic, Sample-Specific, and Compatible Triggers","created":"2023-06-01","cited_by_count":9,"area":null},{"id":"2209.02339","title":"TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World","created":"2022-09-06","cited_by_count":8,"area":null},{"id":"2507.05622","title":"DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective","created":"2025-07-08","cited_by_count":8,"area":null},{"id":"2312.04902","title":"BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting","created":"2023-12-08","cited_by_count":7,"area":null},{"id":"2404.19417","title":"Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical World","created":"2024-04-30","cited_by_count":6,"area":null},{"id":"2203.02079","title":"Dynamic Backdoors with Global Average Pooling","created":"2022-03-04","cited_by_count":5,"area":null},{"id":"2411.12220","title":"DeTrigger: A Gradient-Centric Approach to Backdoor Attack Mitigation in Federated Learning","created":"2024-11-19","cited_by_count":5,"area":null},{"id":"2211.16192","title":"Be Careful with Rotation: A Uniform Backdoor Pattern for 3D Shape","created":"2022-11-28","cited_by_count":4,"area":null},{"id":"2301.08751","title":"Towards Understanding How Self-training Tolerates Data Backdoor Poisoning","created":"2023-01-20","cited_by_count":4,"area":null},{"id":"2312.04584","title":"Towards Sample-specific Backdoor Attack with Clean Labels via Attribute Trigger","created":"2023-12-03","cited_by_count":4,"area":null},{"id":"2408.15591","title":"VFLIP: A Backdoor Defense for Vertical Federated Learning via Identification and Purification","created":"2024-08-28","cited_by_count":4,"area":null},{"id":"2308.11822","title":"PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification","created":"2023-08-22","cited_by_count":3,"area":null},{"id":"2410.18267","title":"Backdoor in Seconds: Unlocking Vulnerabilities in Large Pre-trained Models via Model Editing","created":"2024-10-23","cited_by_count":3,"area":null},{"id":"2510.09647","title":"Rounding-Guided Backdoor Injection in Deep Learning Model Quantization","created":"2025-10-05","cited_by_count":3,"area":null},{"id":"2202.11203","title":"Under-confidence Backdoors Are Resilient and Stealthy Backdoors","created":"2022-02-19","cited_by_count":2,"area":null},{"id":"2309.07428","title":"Physical Invisible Backdoor Based on Camera Imaging","created":"2023-09-14","cited_by_count":2,"area":null},{"id":"2404.13946","title":"Dual Model Replacement:invisible Multi-target Backdoor Attack based on Federal Learning","created":"2024-04-22","cited_by_count":2,"area":null},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2503.12058","title":"Revisiting Training-Inference Trigger Intensity in Backdoor Attacks","created":"2025-03-15","cited_by_count":2,"area":null},{"id":"2510.14299","title":"TED++: Submanifold-Aware Backdoor Detection via Layerwise Tubular-Neighbourhood Screening","created":"2025-10-16","cited_by_count":2,"area":null},{"id":"2205.13613","title":"Circumventing Backdoor Defenses That Are Based on Latent Separability","created":"2022-05-26","cited_by_count":1,"area":null},{"id":"2402.11237","title":"Be Persistent: Towards a Unified Solution for Mitigating Shortcuts in Deep Learning","created":"2024-02-17","cited_by_count":1,"area":null},{"id":"2403.13018","title":"Invisible Backdoor Attack Through Singular Value Decomposition","created":"2024-03-18","cited_by_count":1,"area":null},{"id":"2407.21220","title":"DeepBaR: Fault Backdoor Attack on Deep Neural Network Layers","created":"2024-07-30","cited_by_count":1,"area":null},{"id":"2410.11290","title":"Backdoor Attack on Vertical Federated Graph Neural Network Learning","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2412.03993","title":"LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networks","created":"2024-12-05","cited_by_count":1,"area":null},{"id":"2505.12045","title":"FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2512.19058","title":"6DAttack: Backdoor Attacks in the 6DoF Pose Estimation","created":"2025-12-22","cited_by_count":1,"area":null},{"id":"2602.03586","title":"APEX: Probing Neural Networks via Activation Perturbation","created":"2026-02-03","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":"2302.13056","title":"SATBA: An Invisible Backdoor Attack Based On Spatial Attention","created":"2023-02-25","cited_by_count":0,"area":null},{"id":"2504.05902","title":"Defending against Backdoor Attacks via Module Switching","created":"2025-04-08","cited_by_count":0,"area":null},{"id":"2505.16154","title":"BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World","created":"2025-05-22","cited_by_count":0,"area":null},{"id":"2512.10402","title":"The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2605.22481","title":"When Stronger Triggers Backfire: A High-Dimensional Theory of Backdoor Attacks","created":"2026-05-21","cited_by_count":0,"area":null},{"id":"2607.26099","title":"Lilith: Backdoor Generalization under Training-Inference Trigger Shift","created":"2026-07-28","cited_by_count":0,"area":null}],"next_cursor":null}