{"total":32,"rows":[{"id":"2110.03735","title":"Adversarial Unlearning of Backdoors via Implicit Hypergradient","created":"2021-10-07","cited_by_count":166,"area":null},{"id":"2106.04690","title":"Handcrafted Backdoors in Deep Neural Networks","created":"2021-06-08","cited_by_count":63,"area":null},{"id":"2305.14876","title":"Reconstructive Neuron Pruning for Backdoor Defense","created":"2023-05-24","cited_by_count":59,"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":"2101.03453","title":"BERT & Family Eat Word Salad: Experiments with Text Understanding","created":"2021-01-10","cited_by_count":44,"area":null},{"id":"2108.09187","title":"Quantization Backdoors to Deep Learning Commercial Frameworks","created":"2021-08-20","cited_by_count":29,"area":null},{"id":"2203.07983","title":"Adversarial Robustness of Neural-Statistical Features in Detection of Generative Transformers","created":"2022-03-02","cited_by_count":25,"area":null},{"id":"2109.01300","title":"How to Inject Backdoors with Better Consistency: Logit Anchoring on Clean Data","created":"2021-09-03","cited_by_count":24,"area":null},{"id":"2104.07395","title":"Robust Backdoor Attacks against Deep Neural Networks in Real Physical World","created":"2021-04-15","cited_by_count":20,"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":"2312.08667","title":"Data and Model Poisoning Backdoor Attacks on Wireless Federated Learning, and the Defense Mechanisms: A Comprehensive Survey","created":"2023-12-14","cited_by_count":18,"area":null},{"id":"2210.06895","title":"GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization","created":"2022-10-13","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":"2409.13864","title":"Persistent Backdoor Attacks in Continual Learning","created":"2024-09-20","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":"2204.06273","title":"Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures","created":"2022-04-13","cited_by_count":11,"area":null},{"id":"2212.04687","title":"Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models","created":"2022-12-09","cited_by_count":11,"area":null},{"id":"2106.09993","title":"Accumulative Poisoning Attacks on Real-time Data","created":"2021-06-18","cited_by_count":10,"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":"2110.07683","title":"Don't Knock! Rowhammer at the Backdoor of DNN Models","created":"2021-10-14","cited_by_count":6,"area":null},{"id":"2208.04946","title":"Attention Hijacking in Trojan Transformers","created":"2022-08-09","cited_by_count":6,"area":null},{"id":"2309.01786","title":"Safe and Robust Watermark Injection with a Single OoD Image","created":"2023-09-04","cited_by_count":3,"area":null},{"id":"2311.12764","title":"Investigating Weight-Perturbed Deep Neural Networks With Application in Iris Presentation Attack Detection","created":"2023-11-21","cited_by_count":3,"area":null},{"id":"2405.03884","title":"BadFusion: 2D-Oriented Backdoor Attacks against 3D Object Detection","created":"2024-05-06","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":"2401.03215","title":"End-to-End Anti-Backdoor Learning on Images and Time Series","created":"2024-01-06","cited_by_count":1,"area":null},{"id":"2405.17750","title":"Magnitude-based Neuron Pruning for Backdoor Defens","created":"2024-05-28","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":"2410.19427","title":"Expose Before You Defend: Unifying and Enhancing Backdoor Defenses via Exposed Models","created":"2024-10-25","cited_by_count":1,"area":null},{"id":"2306.13215","title":"OVLA: Neural Network Ownership Verification using Latent Watermarks","created":"2023-06-15","cited_by_count":0,"area":null},{"id":"2512.10402","title":"The Eminence in Shadow: Exploiting Feature Boundary Ambiguity for Robust Backdoor Attacks","created":"2025-12-11","cited_by_count":0,"area":null}],"next_cursor":null}