{"total":25,"rows":[{"id":"2302.09457","title":"Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective","created":"2023-02-19","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":"2407.19845","title":"BackdoorBench: A Comprehensive Benchmark and Analysis of Backdoor Learning","created":"2024-07-29","cited_by_count":19,"area":null},{"id":"2401.02031","title":"Spy-Watermark: Robust Invisible Watermarking for Backdoor Attack","created":"2024-01-04","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":"2307.07328","title":"Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy","created":"2023-07-14","cited_by_count":5,"area":null},{"id":"2412.07231","title":"Adversarial Filtering Based Evasion and Backdoor Attacks to EEG-Based Brain-Computer Interfaces","created":"2024-12-10","cited_by_count":3,"area":null},{"id":"2402.15653","title":"Low-Frequency Black-Box Backdoor Attack via Evolutionary Algorithm","created":"2024-02-23","cited_by_count":2,"area":null},{"id":"2411.19075","title":"LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm","created":"2024-11-28","cited_by_count":2,"area":null},{"id":"2503.06529","title":"AnywhereDoor: Multi-Target Backdoor Attacks on Object Detection","created":"2025-03-09","cited_by_count":2,"area":null},{"id":"2504.21054","title":"FFCBA: Feature-based Full-target Clean-label Backdoor Attacks","created":"2025-04-29","cited_by_count":2,"area":null},{"id":"2302.04977","title":"Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2308.16684","title":"Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack","created":"2023-08-31","cited_by_count":1,"area":null},{"id":"2402.10283","title":"Backdoor Attack against One-Class Sequential Anomaly Detection Models","created":"2024-02-15","cited_by_count":1,"area":null},{"id":"2405.05573","title":"Poisoning-based Backdoor Attacks for Arbitrary Target Label with Positive Triggers","created":"2024-05-09","cited_by_count":1,"area":null},{"id":"2407.03144","title":"Venomancer: Towards Imperceptible and Target-on-Demand Backdoor Attacks in Federated Learning","created":"2024-07-03","cited_by_count":1,"area":null},{"id":"2407.07917","title":"Non-Cooperative Backdoor Attacks in Federated Learning: A New Threat Landscape","created":"2024-07-05","cited_by_count":1,"area":null},{"id":"2506.17350","title":"CUBA: Controlled Untargeted Backdoor Attack against Deep Neural Networks","created":"2025-06-20","cited_by_count":1,"area":null},{"id":"2309.00127","title":"FTA: Stealthy and Adaptive Backdoor Attack with Flexible Triggers on Federated Learning","created":"2023-08-31","cited_by_count":0,"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":"2411.14243","title":"AnywhereDoor: Multi-Target Backdoor Attacks on Object Detection","created":"2024-11-21","cited_by_count":0,"area":null},{"id":"2504.21052","title":"SFIBA: Spatial-based Full-target Invisible Backdoor Attacks","created":"2025-04-29","cited_by_count":0,"area":null},{"id":"2508.04064","title":"FLAT: Revealing Hidden Latent-Conditioned Backdoor Failures in Federated Learning","created":"2025-08-06","cited_by_count":0,"area":null},{"id":"2511.11210","title":"STONE: Pioneering the One-to-N Universal Backdoor Threat in 3D Point Cloud","created":"2025-11-14","cited_by_count":0,"area":null},{"id":"2603.00711","title":"IU: Imperceptible Universal Backdoor Attack","created":"2026-02-28","cited_by_count":0,"area":null}],"next_cursor":null}