{"total":72,"rows":[{"id":"2110.03735","title":"Adversarial Unlearning of Backdoors via Implicit Hypergradient","created":"2021-10-07","cited_by_count":166,"area":null},{"id":"2110.14051","title":"A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges","created":"2021-10-26","cited_by_count":154,"area":null},{"id":"2202.03423","title":"Backdoor Defense via Decoupling the Training Process","created":"2022-02-05","cited_by_count":153,"area":null},{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2102.05123","title":"Backdoor Scanning for Deep Neural Networks through K-Arm Optimization","created":"2021-02-09","cited_by_count":98,"area":null},{"id":"2205.13383","title":"BppAttack: Stealthy and Efficient Trojan Attacks against Deep Neural Networks via Image Quantization and Contrastive Adversarial Learning","created":"2022-05-26","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":"2103.13127","title":"Black-box Detection of Backdoor Attacks with Limited Information and Data","created":"2021-03-24","cited_by_count":73,"area":null},{"id":"2201.09538","title":"Backdoor Defense with Machine Unlearning","created":"2022-01-24","cited_by_count":65,"area":null},{"id":"2106.04690","title":"Handcrafted Backdoors in Deep Neural Networks","created":"2021-06-08","cited_by_count":63,"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":"2303.18191","title":"Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency","created":"2023-03-27","cited_by_count":46,"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":"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":"2112.14889","title":"Few-shot Backdoor Defense Using Shapley Estimation","created":"2021-12-30","cited_by_count":41,"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":"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":"2201.13178","title":"Few-Shot Backdoor Attacks on Visual Object Tracking","created":"2022-01-31","cited_by_count":35,"area":null},{"id":"2201.08474","title":"Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios","created":"2022-01-20","cited_by_count":29,"area":null},{"id":"2302.01762","title":"BackdoorBox: A Python Toolbox for Backdoor Learning","created":"2023-02-01","cited_by_count":26,"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":"2301.01197","title":"Backdoor Attacks Against Dataset Distillation","created":"2023-01-03","cited_by_count":21,"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":"2305.18651","title":"UMD: Unsupervised Model Detection for X2X Backdoor Attacks","created":"2023-05-29","cited_by_count":18,"area":null},{"id":"2202.07183","title":"A Survey of Neural Trojan Attacks and Defenses in Deep Learning","created":"2022-02-15","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":"2301.06241","title":"BEAGLE: Forensics of Deep Learning Backdoor Attack for Better Defense","created":"2023-01-16","cited_by_count":15,"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":"2403.17188","title":"LOTUS: Evasive and Resilient Backdoor Attacks through Sub-Partitioning","created":"2024-03-25","cited_by_count":13,"area":null},{"id":"2109.08868","title":"Backdoor Attack on Hash-based Image Retrieval via Clean-label Data Poisoning","created":"2021-09-18","cited_by_count":12,"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":"2309.16398","title":"Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey","created":"2023-09-28","cited_by_count":12,"area":null},{"id":"2307.15539","title":"Beating Backdoor Attack at Its Own Game","created":"2023-07-28","cited_by_count":11,"area":null},{"id":"2101.02562","title":"DeepPoison: Feature Transfer Based Stealthy Poisoning Attack","created":"2021-01-06","cited_by_count":9,"area":null},{"id":"2411.16110","title":"FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Data","created":"2024-11-25","cited_by_count":8,"area":null},{"id":"2106.05484","title":"A Unified Framework for Task-Driven Data Quality Management","created":"2021-06-10","cited_by_count":7,"area":null},{"id":"2107.09546","title":"Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions","created":"2021-07-20","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":"2103.08820","title":"EX-RAY: Distinguishing Injected Backdoor from Natural Features in Neural Networks by Examining Differential Feature Symmetry","created":"2021-03-16","cited_by_count":5,"area":null},{"id":"2202.07787","title":"Trustworthy Anomaly Detection: A Survey","created":"2022-02-15","cited_by_count":5,"area":null},{"id":"2103.15918","title":"MISA: Online Defense of Trojaned Models using Misattributions","created":"2021-03-29","cited_by_count":4,"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":"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":"2307.15838","title":"Holistic Survey of Privacy and Fairness in Machine Learning","created":"2023-07-28","cited_by_count":4,"area":null},{"id":"2310.19958","title":"PriPrune: Quantifying and Preserving Privacy in Pruned Federated Learning","created":"2023-10-30","cited_by_count":4,"area":null},{"id":"2111.14309","title":"A General Framework for Defending Against Backdoor Attacks via Influence Graph","created":"2021-11-29","cited_by_count":3,"area":null},{"id":"2203.15506","title":"Trojan Horse Training for Breaking Defenses against Backdoor Attacks in Deep Learning","created":"2022-03-25","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":"2312.04740","title":"Train 'n Trade: Foundations of Parameter Markets","created":"2023-12-07","cited_by_count":3,"area":null},{"id":"2401.01168","title":"FedQV: Leveraging Quadratic Voting in Federated Learning","created":"2024-01-02","cited_by_count":3,"area":null},{"id":"2404.06144","title":"Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability","created":"2024-04-09","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":"2503.12058","title":"Revisiting Training-Inference Trigger Intensity in Backdoor Attacks","created":"2025-03-15","cited_by_count":2,"area":null},{"id":"2510.09210","title":"Provable Watermarking for Data Poisoning Attacks","created":"2025-10-10","cited_by_count":2,"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":"2308.09850","title":"Backdoor Mitigation by Correcting the Distribution of Neural Activations","created":"2023-08-18","cited_by_count":1,"area":null},{"id":"2311.06227","title":"Does Differential Privacy Prevent Backdoor Attacks in Practice?","created":"2023-11-10","cited_by_count":1,"area":null},{"id":"2407.07662","title":"Mitigating Backdoor Attacks using Activation-Guided Model Editing","created":"2024-07-10","cited_by_count":1,"area":null},{"id":"2605.19969","title":"Your Neighbors Know: Leveraging Local Neighborhoods for Backdoor Detection in Decentralized Learning","created":"2026-05-19","cited_by_count":1,"area":null},{"id":"2207.05079","title":"Horizontal Federated Learning and Secure Distributed Training for Recommendation System with Intel SGX","created":"2022-07-11","cited_by_count":0,"area":null},{"id":"2306.01342","title":"Covert Communication Based on the Poisoning Attack in Federated Learning","created":"2023-06-02","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":"2601.03805","title":"Detecting Semantic Backdoors in a Mystery Shopping Scenario","created":"2026-01-07","cited_by_count":0,"area":null},{"id":"2605.21780","title":"Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy","created":"2026-05-20","cited_by_count":0,"area":null}],"next_cursor":null}