{"total":105,"rows":[{"id":"2206.12654","title":"BackdoorBench: A Comprehensive Benchmark of Backdoor Learning","created":"2022-06-25","cited_by_count":128,"area":null},{"id":"2210.00875","title":"Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection","created":"2022-09-27","cited_by_count":86,"area":null},{"id":"2208.03111","title":"Data-free Backdoor Removal based on Channel Lipschitzness","created":"2022-08-05","cited_by_count":79,"area":null},{"id":"2304.11823","title":"Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization","created":"2023-04-24","cited_by_count":64,"area":null},{"id":"2305.14876","title":"Reconstructive Neuron Pruning for Backdoor Defense","created":"2023-05-24","cited_by_count":59,"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":"2205.06900","title":"MM-BD: Post-Training Detection of Backdoor Attacks with Arbitrary Backdoor Pattern Types Using a Maximum Margin Statistic","created":"2022-05-13","cited_by_count":55,"area":null},{"id":"2304.02786","title":"UNICORN: A Unified Backdoor Trigger Inversion Framework","created":"2023-04-05","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":"2307.10562","title":"Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples","created":"2023-07-20","cited_by_count":43,"area":null},{"id":"2205.13616","title":"Towards A Proactive ML Approach for Detecting Backdoor Poison Samples","created":"2022-05-26","cited_by_count":40,"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":"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":"2303.06818","title":"Backdoor Defense via Deconfounded Representation Learning","created":"2023-03-13","cited_by_count":38,"area":null},{"id":"2306.16697","title":"Neural Polarizer: A Lightweight and Effective Backdoor Defense via Purifying Poisoned Features","created":"2023-06-29","cited_by_count":37,"area":null},{"id":"2408.07362","title":"BadMerging: Backdoor Attacks Against Model Merging","created":"2024-08-14","cited_by_count":34,"area":null},{"id":"2310.01875","title":"Towards Stable Backdoor Purification through Feature Shift Tuning","created":"2023-10-03","cited_by_count":32,"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":"2302.01762","title":"BackdoorBox: A Python Toolbox for Backdoor Learning","created":"2023-02-01","cited_by_count":26,"area":null},{"id":"2208.08052","title":"Imperceptible and Robust Backdoor Attack in 3D Point Cloud","created":"2022-08-17","cited_by_count":25,"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":"2305.02424","title":"Backdoor Learning on Sequence to Sequence Models","created":"2023-05-03","cited_by_count":21,"area":null},{"id":"2405.11227","title":"BadActs: A Universal Backdoor Defense in the Activation Space","created":"2024-05-18","cited_by_count":21,"area":null},{"id":"2301.10908","title":"Distilling Cognitive Backdoor Patterns within an Image","created":"2023-01-26","cited_by_count":20,"area":null},{"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":"2304.01482","title":"Defending Against Patch-based Backdoor Attacks on Self-Supervised Learning","created":"2023-04-04","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":"2401.15295","title":"Shortcuts Everywhere and Nowhere: Exploring Multi-Trigger Backdoor Attacks","created":"2024-01-27","cited_by_count":19,"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":"2305.18651","title":"UMD: Unsupervised Model Detection for X2X Backdoor Attacks","created":"2023-05-29","cited_by_count":18,"area":null},{"id":"2211.12044","title":"Backdoor Cleansing with Unlabeled Data","created":"2022-11-22","cited_by_count":17,"area":null},{"id":"2302.01677","title":"Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks","created":"2023-02-03","cited_by_count":16,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2310.17498","title":"CBD: A Certified Backdoor Detector Based on Local Dominant Probability","created":"2023-10-26","cited_by_count":13,"area":null},{"id":"2403.10717","title":"Backdoor Secrets Unveiled: Identifying Backdoor Data with Optimized Scaled Prediction Consistency","created":"2024-03-15","cited_by_count":13,"area":null},{"id":"2210.06428","title":"Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork","created":"2022-10-12","cited_by_count":12,"area":null},{"id":"2211.01806","title":"BATT: Backdoor Attack with Transformation-based Triggers","created":"2022-11-02","cited_by_count":11,"area":null},{"id":"2307.15539","title":"Beating Backdoor Attack at Its Own Game","created":"2023-07-28","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":"2405.16112","title":"Mitigating Backdoor Attack by Injecting Proactive Defensive Backdoor","created":"2024-05-25","cited_by_count":11,"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":"2312.09057","title":"On the Difficulty of Defending Contrastive Learning against Backdoor Attacks","created":"2023-12-14","cited_by_count":9,"area":null},{"id":"2310.00542","title":"Watch Out! Simple Horizontal Class Backdoor Can Trivially Evade Defense","created":"2023-10-01","cited_by_count":8,"area":null},{"id":"2310.13424","title":"FLTracer: Accurate Poisoning Attack Provenance in Federated Learning","created":"2023-10-20","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":"2305.10648","title":"Adjusting Logit in Gaussian Form for Long-Tailed Visual Recognition","created":"2023-05-18","cited_by_count":7,"area":null},{"id":"2410.09838","title":"Uncovering, Explaining, and Mitigating the Superficial Safety of Backdoor Defense","created":"2024-10-13","cited_by_count":7,"area":null},{"id":"2411.19479","title":"FLARE: Toward Universal Dataset Purification against Backdoor Attacks","created":"2024-11-29","cited_by_count":7,"area":null},{"id":"2212.11751","title":"Mind Your Heart: Stealthy Backdoor Attack on Dynamic Deep Neural Network in Edge Computing","created":"2022-12-22","cited_by_count":6,"area":null},{"id":"2308.04406","title":"XGBD: Explanation-Guided Graph Backdoor Detection","created":"2023-08-08","cited_by_count":6,"area":null},{"id":"2405.14672","title":"Invisible Backdoor Attack against Self-supervised Learning","created":"2024-05-23","cited_by_count":6,"area":null},{"id":"2405.20291","title":"Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness","created":"2024-05-30","cited_by_count":6,"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":"2405.14781","title":"Unified Neural Backdoor Removal with Only Few Clean Samples through Unlearning and Relearning","created":"2024-05-23","cited_by_count":5,"area":null},{"id":"2406.05826","title":"PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection","created":"2024-06-09","cited_by_count":5,"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":"2301.12318","title":"Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering","created":"2023-01-29","cited_by_count":4,"area":null},{"id":"2407.10825","title":"Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks","created":"2024-07-15","cited_by_count":4,"area":null},{"id":"2210.10272","title":"Training set cleansing of backdoor poisoning by self-supervised representation learning","created":"2022-10-19","cited_by_count":3,"area":null},{"id":"2307.11565","title":"Adversarial Feature Map Pruning for Backdoor","created":"2023-07-21","cited_by_count":3,"area":null},{"id":"2308.11070","title":"Temporal-Distributed Backdoor Attack Against Video Based Action Recognition","created":"2023-08-21","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":"2512.08129","title":"Improving the Sensitivity of Backdoor Detectors via Class Subspace Orthogonalization","created":"2025-12-09","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":"2312.07955","title":"Erasing Self-Supervised Learning Backdoor by Cluster Activation Masking","created":"2023-12-13","cited_by_count":2,"area":null},{"id":"2402.16926","title":"On the (In)feasibility of ML Backdoor Detection as an Hypothesis Testing Problem","created":"2024-02-26","cited_by_count":2,"area":null},{"id":"2405.13080","title":"EmInspector: Combating Backdoor Attacks in Federated Self-Supervised Learning Through Embedding Inspection","created":"2024-05-21","cited_by_count":2,"area":null},{"id":"2407.11372","title":"UNIT: Backdoor Mitigation via Automated Neural Distribution Tightening","created":"2024-07-16","cited_by_count":2,"area":null},{"id":"2409.00863","title":"Fisher Information guided Purification against Backdoor Attacks","created":"2024-09-01","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":"2503.08829","title":"Seal Your Backdoor with Variational Defense","created":"2025-03-11","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":"2510.14299","title":"TED++: Submanifold-Aware Backdoor Detection via Layerwise Tubular-Neighbourhood Screening","created":"2025-10-16","cited_by_count":2,"area":null},{"id":"2205.04411","title":"Model-Contrastive Learning for Backdoor Defense","created":"2022-05-09","cited_by_count":1,"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":"2302.04977","title":"Mithridates: Auditing and Boosting Backdoor Resistance of Machine Learning Pipelines","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2306.17441","title":"Efficient Backdoor Removal Through Natural Gradient Fine-tuning","created":"2023-06-30","cited_by_count":1,"area":null},{"id":"2408.00255","title":"Revocable Backdoor for Deep Model Trading","created":"2024-08-01","cited_by_count":1,"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":"2409.12072","title":"PAD-FT: A Lightweight Defense for Backdoor Attacks via Data Purification and Fine-Tuning","created":"2024-09-18","cited_by_count":1,"area":null},{"id":"2411.18648","title":"MADE: Graph Backdoor Defense with Masked Unlearning","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2412.20476","title":"Cut the Deadwood Out: Backdoor Purification via Guided Module Substitution","created":"2024-12-29","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":"2312.01281","title":"Mendata: A Framework to Purify Manipulated Training Data","created":"2023-12-03","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":"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":"2403.15918","title":"Towards Adversarial Robustness And Backdoor Mitigation in SSL","created":"2024-03-23","cited_by_count":0,"area":null},{"id":"2407.08159","title":"Model-agnostic clean-label backdoor mitigation in cybersecurity environments","created":"2024-07-11","cited_by_count":0,"area":null},{"id":"2407.10180","title":"Defending Against Repetitive Backdoor Attacks on Semi-supervised Learning through Lens of Rate-Distortion-Perception Trade-off","created":"2024-07-14","cited_by_count":0,"area":null},{"id":"2410.14105","title":"DMGNN: Detecting and Mitigating Backdoor Attacks in Graph Neural Networks","created":"2024-10-18","cited_by_count":0,"area":null},{"id":"2501.04108","title":"TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning","created":"2025-01-07","cited_by_count":0,"area":null},{"id":"2506.01444","title":"Variance-Based Defense Against Blended Backdoor Attacks","created":"2025-06-02","cited_by_count":0,"area":null},{"id":"2506.10722","title":"TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2508.05404","title":"NT-ML: Backdoor Defense via Non-target Label Training and Mutual Learning","created":"2025-08-07","cited_by_count":0,"area":null},{"id":"2512.06243","title":"Quantization Blindspots: How Model Compression Breaks Backdoor Defenses","created":"2025-12-06","cited_by_count":0,"area":null},{"id":"2605.18907","title":"Lightweight and Fast Backdoor Model Detection","created":"2026-05-17","cited_by_count":0,"area":null},{"id":"2605.18908","title":"Fast and Lightweight Backdoor Detection via Head Random Probing","created":"2026-05-17","cited_by_count":0,"area":null}],"next_cursor":"100"}