{"total":69,"rows":[{"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":"2104.04448","title":"Relating Adversarially Robust Generalization to Flat Minima","created":"2021-04-09","cited_by_count":57,"area":null},{"id":"2210.09545","title":"Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models","created":"2022-10-18","cited_by_count":57,"area":"safety"},{"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":"2302.14677","title":"Backdoor Attacks Against Deep Image Compression via Adaptive Frequency Trigger","created":"2023-02-28","cited_by_count":40,"area":null},{"id":"2402.08577","title":"Test-Time Backdoor Attacks on Multimodal Large Language Models","created":"2024-02-13","cited_by_count":40,"area":"mllm"},{"id":"2210.09194","title":"Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class","created":"2022-10-17","cited_by_count":33,"area":null},{"id":"2302.04116","title":"Training-free Lexical Backdoor Attacks on Language Models","created":"2023-02-08","cited_by_count":31,"area":"safety"},{"id":"2310.18933","title":"Label Poisoning is All You Need","created":"2023-10-29","cited_by_count":29,"area":"safety"},{"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":"2305.11596","title":"Mitigating Backdoor Poisoning Attacks through the Lens of Spurious Correlation","created":"2023-05-19","cited_by_count":21,"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":"2206.12381","title":"Defending Backdoor Attacks on Vision Transformer via Patch Processing","created":"2022-06-24","cited_by_count":20,"area":"safety"},{"id":"2306.06209","title":"Backdoor Attack with Sparse and Invisible Trigger","created":"2023-05-11","cited_by_count":20,"area":null},{"id":"2102.05257","title":"Robust Federated Learning with Attack-Adaptive Aggregation","created":"2021-02-10","cited_by_count":17,"area":"safety"},{"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":"2503.16023","title":"BadToken: Token-level Backdoor Attacks to Multi-modal Large Language Models","created":"2025-03-20","cited_by_count":17,"area":"safety"},{"id":"2111.05077","title":"Enhancing Backdoor Attacks with Multi-Level MMD Regularization","created":"2021-11-09","cited_by_count":15,"area":null},{"id":"2305.16503","title":"IMBERT: Making BERT Immune to Insertion-based Backdoor Attacks","created":"2023-05-25","cited_by_count":15,"area":"safety"},{"id":"2201.00672","title":"Compression-Resistant Backdoor Attack against Deep Neural Networks","created":"2022-01-03","cited_by_count":14,"area":null},{"id":"2204.12281","title":"Data-Efficient Backdoor Attacks","created":"2022-04-22","cited_by_count":13,"area":null},{"id":"2211.14952","title":"Federated Learning Attacks and Defenses: A Survey","created":"2022-11-27","cited_by_count":12,"area":null},{"id":"2412.07277","title":"Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger","created":"2024-12-10","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":"2412.01646","title":"Robust and Transferable Backdoor Attacks Against Deep Image Compression With Selective Frequency Prior","created":"2024-12-02","cited_by_count":11,"area":null},{"id":"2211.07915","title":"Backdoor Attacks on Time Series: A Generative Approach","created":"2022-11-15","cited_by_count":10,"area":"safety"},{"id":"2201.06202","title":"Neighboring Backdoor Attacks on Graph Convolutional Network","created":"2022-01-17","cited_by_count":7,"area":null},{"id":"2306.08386","title":"Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios","created":"2023-06-14","cited_by_count":7,"area":null},{"id":"2310.09744","title":"Explore the Effect of Data Selection on Poison Efficiency in Backdoor Attacks","created":"2023-10-15","cited_by_count":7,"area":null},{"id":"2408.08671","title":"Towards Physical World Backdoor Attacks against Skeleton Action Recognition","created":"2024-08-16","cited_by_count":7,"area":null},{"id":"2104.08323","title":"Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators","created":"2021-04-16","cited_by_count":6,"area":null},{"id":"2104.10561","title":"Turning Federated Learning Systems Into Covert Channels","created":"2021-04-21","cited_by_count":6,"area":null},{"id":"2209.15139","title":"Augmentation Backdoors","created":"2022-09-29","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":"2309.08953","title":"Robust Backdoor Attacks on Object Detection in Real World","created":"2023-09-16","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":"2301.03118","title":"Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons","created":"2023-01-08","cited_by_count":4,"area":"safety"},{"id":"2302.06801","title":"Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions","created":"2023-02-14","cited_by_count":4,"area":"safety"},{"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":"2205.15944","title":"Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems","created":"2022-05-31","cited_by_count":3,"area":null},{"id":"2304.10985","title":"INK: Inheritable Natural Backdoor Attack Against Model Distillation","created":"2023-04-21","cited_by_count":3,"area":null},{"id":"2308.11822","title":"PatchBackdoor: Backdoor Attack against Deep Neural Networks without Model Modification","created":"2023-08-22","cited_by_count":3,"area":null},{"id":"2405.09550","title":"Mask-based Invisible Backdoor Attacks on Object Detection","created":"2024-03-20","cited_by_count":3,"area":null},{"id":"2411.03022","title":"Flashy Backdoor: Real-world Environment Backdoor Attack on SNNs with DVS Cameras","created":"2024-11-05","cited_by_count":3,"area":null},{"id":"2510.09647","title":"Rounding-Guided Backdoor Injection in Deep Learning Model Quantization","created":"2025-10-05","cited_by_count":3,"area":null},{"id":"2203.09289","title":"PiDAn: A Coherence Optimization Approach for Backdoor Attack Detection and Mitigation in Deep Neural Networks","created":"2022-03-17","cited_by_count":2,"area":null},{"id":"2405.00466","title":"Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable","created":"2024-05-01","cited_by_count":2,"area":"diffusion"},{"id":"2409.03077","title":"Backdoor defense, learnability and obfuscation","created":"2024-09-04","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":"2503.23748","title":"THEMIS: Towards Practical Intellectual Property Protection for Post-Deployment On-Device Deep Learning Models","created":"2025-03-31","cited_by_count":2,"area":"safety"},{"id":"2508.18235","title":"Sealing The Backdoor: Unlearning Adversarial Text Triggers In Diffusion Models Using Knowledge Distillation","created":"2025-08-20","cited_by_count":2,"area":"diffusion"},{"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":"2305.05503","title":"BadCS: A Backdoor Attack Framework for Code search","created":"2023-05-09","cited_by_count":1,"area":"safety"},{"id":"2308.16684","title":"Everyone Can Attack: Repurpose Lossy Compression as a Natural Backdoor Attack","created":"2023-08-31","cited_by_count":1,"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":"2402.08695","title":"Game of Trojans: Adaptive Adversaries Against Output-based Trojaned-Model Detectors","created":"2024-02-12","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":"2411.17283","title":"BadScan: An Architectural Backdoor Attack on Visual State Space Models","created":"2024-11-26","cited_by_count":1,"area":null},{"id":"2501.01263","title":"Stealthy Backdoor Attack to Real-world Models in Android Apps","created":"2025-01-02","cited_by_count":1,"area":null},{"id":"2505.12019","title":"FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients","created":"2025-05-17","cited_by_count":1,"area":null},{"id":"2207.05937","title":"Game of Trojans: A Submodular Byzantine Approach","created":"2022-07-13","cited_by_count":0,"area":null},{"id":"2402.00896","title":"Privacy and Security Implications of Cloud-Based AI Services : A Survey","created":"2024-01-31","cited_by_count":0,"area":null},{"id":"2406.12929","title":"RMF: A Risk Measurement Framework for Machine Learning Models","created":"2024-06-15","cited_by_count":0,"area":null},{"id":"2408.08920","title":"A Survey of Trojan Attacks and Defenses to Deep Neural Networks","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2410.09470","title":"Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks","created":"2024-10-12","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":"2506.10722","title":"TED-LaST: Towards Robust Backdoor Defense Against Adaptive Attacks","created":"2025-06-12","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}