{"list":"other","total":66,"rows":[{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"id":"1708.06733","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","created":"2017-08-22","cited_by_count":1106},{"id":"1912.03817","title":"Machine Unlearning","created":"2019-12-09","cited_by_count":1061},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"2003.14053","title":"Inverting Gradients -- How easy is it to break privacy in federated learning?","created":"2020-03-31","cited_by_count":712},{"id":"2306.08302","title":"Unifying Large Language Models and Knowledge Graphs: A Roadmap","created":"2023-06-14","cited_by_count":668},{"id":"1708.03999","title":"ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models","created":"2017-08-14","cited_by_count":666},{"id":"2403.03218","title":"The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning","created":"2024-03-05","cited_by_count":509},{"id":"2007.08745","title":"Backdoor Learning: A Survey","created":"2020-07-17","cited_by_count":426},{"id":"1810.00069","title":"Adversarial Attacks and Defences: A Survey","created":"2018-09-28","cited_by_count":259},{"id":"2310.11868","title":"To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now","created":"2023-10-18","cited_by_count":210},{"id":"2005.02205","title":"When Machine Unlearning Jeopardizes Privacy","created":"2020-05-05","cited_by_count":198},{"id":"2307.08715","title":"MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots","created":"2023-07-16","cited_by_count":183},{"id":"2110.03735","title":"Adversarial Unlearning of Backdoors via Implicit Hypergradient","created":"2021-10-07","cited_by_count":166},{"id":"2009.02276","title":"Witches' Brew: Industrial Scale Data Poisoning via Gradient Matching","created":"2020-09-04","cited_by_count":163},{"id":"1908.01763","title":"TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems","created":"2019-08-02","cited_by_count":122},{"id":"2203.07320","title":"The Right to be Forgotten in Federated Learning: An Efficient Realization with Rapid Retraining","created":"2022-03-14","cited_by_count":118},{"id":"2110.11794","title":"Federated Unlearning via Class-Discriminative Pruning","created":"2021-10-22","cited_by_count":115},{"id":"2207.05521","title":"Federated Unlearning: How to Efficiently Erase a Client in FL?","created":"2022-07-12","cited_by_count":95},{"id":"2402.09063","title":"Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space","created":"2024-02-14","cited_by_count":91},{"id":"2105.00164","title":"Hidden Backdoors in Human-Centric Language Models","created":"2021-05-01","cited_by_count":90},{"id":"2201.09441","title":"Federated Unlearning with Knowledge Distillation","created":"2022-01-24","cited_by_count":73},{"id":"2201.09538","title":"Backdoor Defense with Machine Unlearning","created":"2022-01-24","cited_by_count":65},{"id":"2209.11715","title":"The \"Beatrix'' Resurrections: Robust Backdoor Detection via Gram Matrices","created":"2022-09-23","cited_by_count":63},{"id":"2303.03323","title":"CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning","created":"2023-03-06","cited_by_count":63},{"id":"2310.20448","title":"A Survey on Federated Unlearning: Challenges, Methods, and Future Directions","created":"2023-10-31","cited_by_count":63},{"id":"2003.10933","title":"Learn to Forget: Machine Unlearning via Neuron Masking","created":"2020-03-24","cited_by_count":62},{"id":"2210.10936","title":"FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information","created":"2022-10-20","cited_by_count":58},{"id":"2003.04247","title":"Towards Probabilistic Verification of Machine Unlearning","created":"2020-03-09","cited_by_count":50},{"id":"2108.02488","title":"Poison Ink: Robust and Invisible Backdoor Attack","created":"2021-08-05","cited_by_count":50},{"id":"2212.10717","title":"Hidden Poison: Machine Unlearning Enables Camouflaged Poisoning Attacks","created":"2022-12-21","cited_by_count":43},{"id":"2302.13406","title":"GNNDelete: A General Strategy for Unlearning in Graph Neural Networks","created":"2023-02-26","cited_by_count":43},{"id":"2307.10562","title":"Shared Adversarial Unlearning: Backdoor Mitigation by Unlearning Shared Adversarial Examples","created":"2023-07-20","cited_by_count":43},{"id":"2104.03674","title":"Explainability-based Backdoor Attacks Against Graph Neural Networks","created":"2021-04-08","cited_by_count":42},{"id":"1711.07221","title":"Model Extraction Warning in MLaaS Paradigm","created":"2017-11-20","cited_by_count":40},{"id":"2108.01250","title":"Your fairness may vary: Pretrained language model fairness in toxic text classification","created":"2021-08-03","cited_by_count":39},{"id":"2111.09999","title":"TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems","created":"2021-11-19","cited_by_count":36},{"id":"1910.02007","title":"PPGAN: Privacy-preserving Generative Adversarial Network","created":"2019-10-04","cited_by_count":34},{"id":"2307.01709","title":"Dipping PLMs Sauce: Bridging Structure and Text for Effective Knowledge Graph Completion via Conditional Soft Prompting","created":"2023-07-04","cited_by_count":33},{"id":"2209.07299","title":"Knowledge Is Flat: A Seq2Seq Generative Framework for Various Knowledge Graph Completion","created":"2022-09-15","cited_by_count":32},{"id":"2203.17005","title":"Privacy-Preserving Aggregation in Federated Learning: A Survey","created":"2022-03-31","cited_by_count":27},{"id":"2401.05146","title":"Federated Unlearning: A Survey on Methods, Design Guidelines, and Evaluation Metrics","created":"2024-01-10","cited_by_count":27},{"id":"2401.00676","title":"Digger: Detecting Copyright Content Mis-usage in Large Language Model Training","created":"2024-01-01","cited_by_count":25},{"id":"2203.16000","title":"StyleFool: Fooling Video Classification Systems via Style Transfer","created":"2022-03-30","cited_by_count":20},{"id":"2203.17044","title":"Efficient Dropout-resilient Aggregation for Privacy-preserving Machine Learning","created":"2022-03-31","cited_by_count":20},{"id":"2006.05096","title":"MLModelCI: An Automatic Cloud Platform for Efficient MLaaS","created":"2020-06-09","cited_by_count":16},{"id":"2310.05847","title":"Making Users Indistinguishable: Attribute-wise Unlearning in Recommender Systems","created":"2023-10-06","cited_by_count":15},{"id":"2308.10718","title":"Backdooring Textual Inversion for Concept Censorship","created":"2023-08-21","cited_by_count":14},{"id":"2402.10695","title":"Unlink to Unlearn: Simplifying Edge Unlearning in GNNs","created":"2024-02-16","cited_by_count":11},{"id":"2311.14705","title":"Ethics and Responsible AI Deployment","created":"2023-11-12","cited_by_count":8},{"id":"2312.01235","title":"Strategic Data Revocation in Federated Unlearning","created":"2023-12-02","cited_by_count":7},{"id":"2312.09935","title":"LogoStyleFool: Vitiating Video Recognition Systems via Logo Style Transfer","created":"2023-12-15","cited_by_count":7},{"id":"2402.01276","title":"Federated Unlearning: a Perspective of Stability and Fairness","created":"2024-02-02","cited_by_count":7},{"id":"2401.15917","title":"Blockchain-enabled Trustworthy Federated Unlearning","created":"2024-01-29","cited_by_count":6},{"id":"2401.15957","title":"Scalable Federated Unlearning via Isolated and Coded Sharding","created":"2024-01-29","cited_by_count":6},{"id":"2007.12557","title":"MPC-enabled Privacy-Preserving Neural Network Training against Malicious Attack","created":"2020-07-24","cited_by_count":4},{"id":"2303.09008","title":"Not Seen, Not Heard in the Digital World! Measuring Privacy Practices in Children's Apps","created":"2023-03-16","cited_by_count":4},{"id":"2310.10659","title":"Exploiting Machine Unlearning for Backdoor Attacks in Deep Learning System","created":"2023-09-12","cited_by_count":4},{"id":"2312.10578","title":"SAME: Sample Reconstruction against Model Extraction Attacks","created":"2023-12-17","cited_by_count":4},{"id":"2311.02324","title":"Bounded and Unbiased Composite Differential Privacy","created":"2023-11-04","cited_by_count":3},{"id":"2308.12502","title":"Incentivized Federated Learning and Unlearning","created":"2023-08-24","cited_by_count":2},{"id":"2403.11656","title":"LocalStyleFool: Regional Video Style Transfer Attack Using Segment Anything Model","created":"2024-03-18","cited_by_count":2},{"id":"2301.10028","title":"Super forecasting the technological singularity risks from artificial intelligence","created":"2022-12-28","cited_by_count":1},{"id":"2307.05627","title":"Separate-and-Aggregate: A Transformer-based Patch Refinement Model for Knowledge Graph Completion","created":"2023-07-11","cited_by_count":1}],"next_cursor":null}