{"total":69,"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":"2102.05123","title":"Backdoor Scanning for Deep Neural Networks through K-Arm Optimization","created":"2021-02-09","cited_by_count":98,"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":"2112.01148","title":"FIBA: Frequency-Injection based Backdoor Attack in Medical Image Analysis","created":"2021-12-02","cited_by_count":61,"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":"2110.14880","title":"AEVA: Black-box Backdoor Detection Using Adversarial Extreme Value Analysis","created":"2021-10-28","cited_by_count":53,"area":null},{"id":"2108.02488","title":"Poison Ink: Robust and Invisible Backdoor Attack","created":"2021-08-05","cited_by_count":50,"area":null},{"id":"2205.08305","title":"A Study of the Attention Abnormality in Trojaned BERTs","created":"2022-05-13","cited_by_count":48,"area":null},{"id":"2210.12873","title":"FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning","created":"2022-10-23","cited_by_count":37,"area":null},{"id":"2106.06469","title":"Topological Detection of Trojaned Neural Networks","created":"2021-06-11","cited_by_count":34,"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":"2207.04497","title":"One-shot Neural Backdoor Erasing via Adversarial Weight Masking","created":"2022-07-10","cited_by_count":28,"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":"2301.10908","title":"Distilling Cognitive Backdoor Patterns within an Image","created":"2023-01-26","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":"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":"2209.05244","title":"Universal Backdoor Attacks Detection via Adaptive Adversarial Probe","created":"2022-09-12","cited_by_count":16,"area":null},{"id":"2205.11819","title":"Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free","created":"2022-05-24","cited_by_count":15,"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":"2308.07934","title":"One-bit Flip is All You Need: When Bit-flip Attack Meets Model Training","created":"2023-08-12","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":"2103.10274","title":"TOP: Backdoor Detection in Neural Networks via Transferability of Perturbation","created":"2021-03-18","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":"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":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2110.10354","title":"Detecting Backdoor Attacks Against Point Cloud Classifiers","created":"2021-10-20","cited_by_count":10,"area":null},{"id":"2303.00215","title":"Single Image Backdoor Inversion via Robust Smoothed Classifiers","created":"2023-03-01","cited_by_count":10,"area":null},{"id":"2202.12154","title":"Towards Effective and Robust Neural Trojan Defenses via Input Filtering","created":"2022-02-24","cited_by_count":7,"area":null},{"id":"2208.04946","title":"Attention Hijacking in Trojan Transformers","created":"2022-08-09","cited_by_count":6,"area":null},{"id":"2210.06704","title":"COLLIDER: A Robust Training Framework for Backdoor Data","created":"2022-10-13","cited_by_count":6,"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":"2301.03052","title":"AI Maintenance: A Robustness Perspective","created":"2023-01-08","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":"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":"2406.05826","title":"PSBD: Prediction Shift Uncertainty Unlocks Backdoor Detection","created":"2024-06-09","cited_by_count":5,"area":null},{"id":"2410.11205","title":"Adversarially Guided Stateful Defense Against Backdoor Attacks in Federated Deep Learning","created":"2024-10-15","cited_by_count":5,"area":null},{"id":"2503.21305","title":"DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data","created":"2025-03-27","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":"2208.06537","title":"Defense against Backdoor Attacks via Identifying and Purifying Bad Neurons","created":"2022-08-13","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":"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":"2205.09167","title":"Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution","created":"2022-05-18","cited_by_count":3,"area":null},{"id":"2403.08208","title":"Advancing Security in AI Systems: A Novel Approach to Detecting Backdoors in Deep Neural Networks","created":"2024-03-13","cited_by_count":3,"area":null},{"id":"2407.10052","title":"Augmented Neural Fine-Tuning for Efficient Backdoor Purification","created":"2024-07-14","cited_by_count":3,"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":"2108.11299","title":"Certifiers Make Neural Networks Vulnerable to Availability Attacks","created":"2021-08-25","cited_by_count":1,"area":null},{"id":"2112.13064","title":"CatchBackdoor: Backdoor Detection via Critical Trojan Neural Path Fuzzing","created":"2021-12-24","cited_by_count":1,"area":null},{"id":"2210.06509","title":"Understanding Impacts of Task Similarity on Backdoor Attack and Detection","created":"2022-10-12","cited_by_count":1,"area":null},{"id":"2306.06414","title":"Revealing Model Biases: Assessing Deep Neural Networks via Recovered Sample Analysis","created":"2023-06-10","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":"2402.02034","title":"CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors","created":"2024-02-03","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":"2108.06797","title":"Deep Adversarially-Enhanced k-Nearest Neighbors","created":"2021-08-15","cited_by_count":0,"area":null},{"id":"2110.03106","title":"Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning","created":"2021-10-06","cited_by_count":0,"area":null},{"id":"2308.09487","title":"DFB: A Data-Free, Low-Budget, and High-Efficacy Clean-Label Backdoor Attack","created":"2023-08-18","cited_by_count":0,"area":null},{"id":"2310.16224","title":"Poison is Not Traceless: Fully-Agnostic Detection of Poisoning Attacks","created":"2023-10-24","cited_by_count":0,"area":null},{"id":"2405.16488","title":"Partial train and isolate, mitigate backdoor attack","created":"2024-05-26","cited_by_count":0,"area":null},{"id":"2502.10490","title":"A Robust Attack: Displacement Backdoor Attack","created":"2025-02-14","cited_by_count":0,"area":null},{"id":"2502.18592","title":"DeBUGCN -- Detecting Backdoors in CNNs Using Graph Convolutional Networks","created":"2025-02-25","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":"2601.03805","title":"Detecting Semantic Backdoors in a Mystery Shopping Scenario","created":"2026-01-07","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":null}