{"total":75,"rows":[{"id":"2110.14430","title":"Adversarial Neuron Pruning Purifies Backdoored Deep Models","created":"2021-10-27","cited_by_count":235,"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":"2104.11315","title":"SPECTRE: Defending Against Backdoor Attacks Using Robust Statistics","created":"2021-04-22","cited_by_count":130,"area":null},{"id":"2106.08283","title":"CRFL: Certifiably Robust Federated Learning against Backdoor Attacks","created":"2021-06-15","cited_by_count":91,"area":null},{"id":"2111.00197","title":"Backdoor Pre-trained Models Can Transfer to All","created":"2021-10-30","cited_by_count":86,"area":null},{"id":"2102.04716","title":"Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training","created":"2021-02-09","cited_by_count":59,"area":null},{"id":"2112.06274","title":"SparseFed: Mitigating Model Poisoning Attacks in Federated Learning with Sparsification","created":"2021-12-12","cited_by_count":46,"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":"2103.10834","title":"Improved, Deterministic Smoothing for L_1 Certified Robustness","created":"2021-03-17","cited_by_count":39,"area":null},{"id":"2103.02079","title":"DP-InstaHide: Provably Defusing Poisoning and Backdoor Attacks with Differentially Private Data Augmentations","created":"2021-03-02","cited_by_count":34,"area":null},{"id":"2201.10055","title":"Identifying a Training-Set Attack's Target Using Renormalized Influence Estimation","created":"2022-01-25","cited_by_count":32,"area":null},{"id":"2207.04497","title":"One-shot Neural Backdoor Erasing via Adversarial Weight Masking","created":"2022-07-10","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":"2405.03316","title":"Provably Unlearnable Data Examples","created":"2024-05-06","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":"2202.10627","title":"On the Effectiveness of Adversarial Training against Backdoor Attacks","created":"2022-02-22","cited_by_count":16,"area":null},{"id":"2210.05929","title":"Few-shot Backdoor Attacks via Neural Tangent Kernels","created":"2022-10-12","cited_by_count":15,"area":null},{"id":"2202.05470","title":"Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers","created":"2022-02-11","cited_by_count":14,"area":null},{"id":"2312.08890","title":"Defenses in Adversarial Machine Learning: A Survey","created":"2023-12-13","cited_by_count":14,"area":null},{"id":"2105.08709","title":"Learning and Certification under Instance-targeted Poisoning","created":"2021-05-18","cited_by_count":13,"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":"2312.08898","title":"Detection and Defense of Unlearnable Examples","created":"2023-12-14","cited_by_count":13,"area":null},{"id":"2209.04030","title":"Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks","created":"2022-09-08","cited_by_count":12,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2109.10870","title":"SoK: Machine Learning Governance","created":"2021-09-20","cited_by_count":10,"area":null},{"id":"2411.04376","title":"Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging","created":"2024-11-07","cited_by_count":10,"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":"2106.01425","title":"GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations","created":"2021-06-02","cited_by_count":8,"area":null},{"id":"2312.03979","title":"Node-aware Bi-smoothing: Certified Robustness against Graph Injection Attacks","created":"2023-12-07","cited_by_count":8,"area":null},{"id":"2205.03824","title":"A Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges","created":"2022-05-08","cited_by_count":7,"area":null},{"id":"2208.13904","title":"Reducing Certified Regression to Certified Classification for General Poisoning Attacks","created":"2022-08-29","cited_by_count":7,"area":null},{"id":"2406.09836","title":"Robustness Inspired Graph Backdoor Defense","created":"2024-06-14","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":"2112.10525","title":"Certified Federated Adversarial Training","created":"2021-12-20","cited_by_count":6,"area":null},{"id":"2308.07553","title":"Enhancing the Antidote: Improved Pointwise Certifications against Poisoning Attacks","created":"2023-08-15","cited_by_count":6,"area":null},{"id":"2312.04542","title":"SoK: Unintended Interactions among Machine Learning Defenses and Risks","created":"2023-12-07","cited_by_count":6,"area":null},{"id":"2403.18136","title":"Identifying Backdoored Graphs in Graph Neural Network Training: An Explanation-Based Approach with Novel Metrics","created":"2024-03-26","cited_by_count":6,"area":null},{"id":"2205.11736","title":"Towards a Defense Against Federated Backdoor Attacks Under Continuous Training","created":"2022-05-24","cited_by_count":5,"area":null},{"id":"2301.04554","title":"Universal Detection of Backdoor Attacks via Density-based Clustering and Centroids Analysis","created":"2023-01-11","cited_by_count":5,"area":null},{"id":"2301.11824","title":"PECAN: A Deterministic Certified Defense Against Backdoor Attacks","created":"2023-01-27","cited_by_count":5,"area":null},{"id":"2303.12054","title":"Influencer Backdoor Attack on Semantic Segmentation","created":"2023-03-21","cited_by_count":5,"area":null},{"id":"2407.10867","title":"Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks","created":"2024-07-15","cited_by_count":5,"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":"2504.21730","title":"Cert-SSBD: Certified Backdoor Defense with Sample-Specific Smoothing Noises","created":"2025-04-30","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":"2409.17328","title":"The poison of dimensionality","created":"2024-09-25","cited_by_count":3,"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":"2406.11522","title":"FullCert: Deterministic End-to-End Certification for Training and Inference of Neural Networks","created":"2024-06-17","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":"2506.03075","title":"Agnostic Learning under Targeted Poisoning: Optimal Rates and the Role of Randomness","created":"2025-06-03","cited_by_count":2,"area":null},{"id":"2509.25979","title":"Reconcile Certified Robustness and Accuracy for DNN-based Smoothed Majority Vote Classifier","created":"2025-09-30","cited_by_count":2,"area":null},{"id":"2210.13235","title":"Chaos Theory and Adversarial Robustness","created":"2022-10-20","cited_by_count":1,"area":null},{"id":"2212.09979","title":"Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation","created":"2022-12-20","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":"2312.14461","title":"Attacking Byzantine Robust Aggregation in High Dimensions","created":"2023-12-22","cited_by_count":1,"area":null},{"id":"2403.11981","title":"Certified Robustness to Clean-Label Poisoning Using Diffusion Denoising","created":"2024-03-18","cited_by_count":1,"area":null},{"id":"2404.14042","title":"CloudFort: Enhancing Robustness of 3D Point Cloud Classification Against Backdoor Attacks via Spatial Partitioning and Ensemble Prediction","created":"2024-04-22","cited_by_count":1,"area":null},{"id":"2406.01873","title":"CR-UTP: Certified Robustness against Universal Text Perturbations on Large Language Models","created":"2024-06-04","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":"2504.00170","title":"Backdoor Detection through Replicated Execution of Outsourced Training","created":"2025-03-31","cited_by_count":1,"area":null},{"id":"2510.06629","title":"Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks","created":"2025-10-08","cited_by_count":1,"area":null},{"id":"2101.08177","title":"On Provable Backdoor Defense in Collaborative Learning","created":"2021-01-19","cited_by_count":0,"area":null},{"id":"2202.10320","title":"Resurrecting Trust in Facial Recognition: Mitigating Backdoor Attacks in Face Recognition to Prevent Potential Privacy Breaches","created":"2022-02-18","cited_by_count":0,"area":null},{"id":"2412.08366","title":"Backdoor attacks on DNN and GBDT -- A Case Study from the insurance domain","created":"2024-12-11","cited_by_count":0,"area":null},{"id":"2412.10186","title":"MIBP-Cert: Certified Training against Data Perturbations with Mixed-Integer Bilinear Programs","created":"2024-12-13","cited_by_count":0,"area":null},{"id":"2502.01272","title":"Boosting Graph Robustness Against Backdoor Attacks: An Over-Similarity Perspective","created":"2025-02-03","cited_by_count":0,"area":null},{"id":"2503.17172","title":"Principal Eigenvalue Regularization for Improved Worst-Class Certified Robustness of Smoothed Classifiers","created":"2025-03-21","cited_by_count":0,"area":null},{"id":"2602.00183","title":"RPP: A Certified Poisoned-Sample Detection Framework for Backdoor Attacks under Dataset Imbalance","created":"2026-01-30","cited_by_count":0,"area":null},{"id":"2603.10641","title":"Detecting and Eliminating Neural Network Backdoors Through Active Paths with Application to Intrusion Detection","created":"2026-03-11","cited_by_count":0,"area":null},{"id":"2604.11416","title":"Exact Certification of Neural Networks and Partition Aggregation Ensembles against Label Poisoning","created":"2026-04-13","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},{"id":"2610.13051","title":"Space Over Statistic, Retraining Over Ranking: Auditing Training Labels Under Label-Only Backdoors","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}