{"list":"other","total":152,"rows":[{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1412.6572","title":"Explaining and Harnessing Adversarial Examples","created":"2014-12-20","cited_by_count":7364},{"id":"1706.06083","title":"Towards Deep Learning Models Resistant to Adversarial Attacks","created":"2017-06-19","cited_by_count":6415},{"id":"1312.6199","title":"Intriguing properties of neural networks","created":"2013-12-21","cited_by_count":5155},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1607.00133","title":"Deep Learning with Differential Privacy","created":"2016-07-01","cited_by_count":2728},{"id":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"2012.07805","title":"Extracting Training Data from Large Language Models","created":"2020-12-14","cited_by_count":2189},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1712.05526","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","created":"2017-12-15","cited_by_count":1055},{"id":"1906.08935","title":"Deep Leakage from Gradients","created":"2019-06-21","cited_by_count":1021},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"2112.03570","title":"Membership Inference Attacks From First Principles","created":"2021-12-07","cited_by_count":878},{"id":"1802.08232","title":"The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks","created":"2018-02-22","cited_by_count":864},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"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":"1609.02943","title":"Stealing Machine Learning Models via Prediction APIs","created":"2016-09-09","cited_by_count":619},{"id":"1710.08864","title":"One pixel attack for fooling deep neural networks","created":"2017-10-24","cited_by_count":582},{"id":"1805.04049","title":"Exploiting Unintended Feature Leakage in Collaborative Learning","created":"2018-05-10","cited_by_count":520},{"id":"1804.00792","title":"Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks","created":"2018-04-03","cited_by_count":519},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"1806.01246","title":"ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models","created":"2018-06-04","cited_by_count":498},{"id":"1911.11815","title":"Local Model Poisoning Attacks to Byzantine-Robust Federated Learning","created":"2019-11-26","cited_by_count":493},{"id":"1704.01155","title":"Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks","created":"2017-04-04","cited_by_count":486},{"id":"1712.01887","title":"Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training","created":"2017-12-05","cited_by_count":478},{"id":"1712.04248","title":"Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models","created":"2017-12-12","cited_by_count":462},{"id":"1712.07557","title":"Differentially Private Federated Learning: A Client Level Perspective","created":"2017-12-20","cited_by_count":459},{"id":"1805.07984","title":"Adversarial Attacks on Neural Networks for Graph Data","created":"2018-05-21","cited_by_count":397},{"id":"2007.14321","title":"Label-Only Membership Inference Attacks","created":"2020-07-28","cited_by_count":388},{"id":"2001.02610","title":"iDLG: Improved Deep Leakage from Gradients","created":"2020-01-08","cited_by_count":382},{"id":"1811.12470","title":"Analyzing Federated Learning through an Adversarial Lens","created":"2018-11-29","cited_by_count":351},{"id":"2004.06660","title":"Weight Poisoning Attacks on Pre-trained Models","created":"2020-04-14","cited_by_count":341},{"id":"1702.07464","title":"Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning","created":"2017-02-24","cited_by_count":334},{"id":"1904.02144","title":"HopSkipJumpAttack: A Query-Efficient Decision-Based Attack","created":"2019-04-03","cited_by_count":333},{"id":"1702.02284","title":"Adversarial Attacks on Neural Network Policies","created":"2017-02-08","cited_by_count":320},{"id":"1812.02766","title":"Knockoff Nets: Stealing Functionality of Black-Box Models","created":"2018-12-06","cited_by_count":292},{"id":"2003.10595","title":"Systematic Evaluation of Privacy Risks of Machine Learning Models","created":"2020-03-24","cited_by_count":292},{"id":"2007.08432","title":"Data Poisoning Attacks Against Federated Learning Systems","created":"2020-07-16","cited_by_count":275},{"id":"1908.11229","title":"White-box vs Black-box: Bayes Optimal Strategies for Membership Inference","created":"2019-08-29","cited_by_count":271},{"id":"1705.09064","title":"MagNet: a Two-Pronged Defense against Adversarial Examples","created":"2017-05-25","cited_by_count":269},{"id":"1610.06940","title":"Safety Verification of Deep Neural Networks","created":"2016-10-21","cited_by_count":263},{"id":"1905.07121","title":"Simple Black-box Adversarial Attacks","created":"2019-05-17","cited_by_count":251},{"id":"2111.09679","title":"Enhanced Membership Inference Attacks against Machine Learning Models","created":"2021-11-18","cited_by_count":248},{"id":"1812.00535","title":"Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning","created":"2018-12-03","cited_by_count":246},{"id":"1911.07135","title":"The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks","created":"2019-11-17","cited_by_count":246},{"id":"1703.00410","title":"Detecting Adversarial Samples from Artifacts","created":"2017-03-01","cited_by_count":245},{"id":"1702.04267","title":"On Detecting Adversarial Perturbations","created":"2017-02-14","cited_by_count":232},{"id":"1804.00308","title":"Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning","created":"2018-04-01","cited_by_count":225},{"id":"1909.01838","title":"High Accuracy and High Fidelity Extraction of Neural Networks","created":"2019-09-03","cited_by_count":214},{"id":"2005.02205","title":"When Machine Unlearning Jeopardizes Privacy","created":"2020-05-05","cited_by_count":198},{"id":"1909.10594","title":"MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples","created":"2019-09-23","cited_by_count":197},{"id":"1807.05852","title":"Machine Learning with Membership Privacy using Adversarial Regularization","created":"2018-07-16","cited_by_count":186},{"id":"1702.06280","title":"On the (Statistical) Detection of Adversarial Examples","created":"2017-02-21","cited_by_count":180},{"id":"1611.04482","title":"Practical Secure Aggregation for Federated Learning on User-Held Data","created":"2016-11-14","cited_by_count":178},{"id":"1906.11798","title":"Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference","created":"2019-06-27","cited_by_count":175},{"id":"1709.07886","title":"Machine Learning Models that Remember Too Much","created":"2017-09-22","cited_by_count":169},{"id":"1805.02628","title":"PRADA: Protecting against DNN Model Stealing Attacks","created":"2018-05-07","cited_by_count":165},{"id":"1604.04326","title":"Improving the Robustness of Deep Neural Networks via Stability Training","created":"2016-04-15","cited_by_count":156},{"id":"2102.00436","title":"Admix: Enhancing the Transferability of Adversarial Attacks","created":"2021-01-31","cited_by_count":156},{"id":"1710.00942","title":"Neural Trojans","created":"2017-10-03","cited_by_count":153},{"id":"2201.04845","title":"Reconstructing Training Data with Informed Adversaries","created":"2022-01-13","cited_by_count":148},{"id":"1905.10291","title":"Privacy Risks of Securing Machine Learning Models against Adversarial Examples","created":"2019-05-24","cited_by_count":141},{"id":"1910.12366","title":"Thieves on Sesame Street! Model Extraction of BERT-based APIs","created":"2019-10-27","cited_by_count":139},{"id":"2109.00984","title":"CrypTen: Secure Multi-Party Computation Meets Machine Learning","created":"2021-09-02","cited_by_count":131},{"id":"1306.4447","title":"Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers","created":"2013-06-19","cited_by_count":130},{"id":"1807.04457","title":"Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach","created":"2018-07-12","cited_by_count":130},{"id":"2011.14779","title":"Data-Free Model Extraction","created":"2020-11-30","cited_by_count":124},{"id":"1904.01067","title":"Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning","created":"2019-04-01","cited_by_count":122},{"id":"1909.10773","title":"Sign-OPT: A Query-Efficient Hard-label Adversarial Attack","created":"2019-09-24","cited_by_count":120},{"id":"1802.05351","title":"Stealing Hyperparameters in Machine Learning","created":"2018-02-14","cited_by_count":118},{"id":"1809.01093","title":"Adversarial Attacks on Node Embeddings via Graph Poisoning","created":"2018-09-04","cited_by_count":109},{"id":"2012.02670","title":"Unleashing the Tiger: Inference Attacks on Split Learning","created":"2020-12-04","cited_by_count":105},{"id":"2005.14137","title":"QEBA: Query-Efficient Boundary-Based Blackbox Attack","created":"2020-05-28","cited_by_count":104},{"id":"2208.14933","title":"Membership Inference Attacks by Exploiting Loss Trajectory","created":"2022-08-31","cited_by_count":101},{"id":"2010.07733","title":"R-GAP: Recursive Gradient Attack on Privacy","created":"2020-10-15","cited_by_count":97},{"id":"2203.06616","title":"LAS-AT: Adversarial Training with Learnable Attack Strategy","created":"2022-03-13","cited_by_count":95},{"id":"1802.04889","title":"Understanding Membership Inferences on Well-Generalized Learning Models","created":"2018-02-13","cited_by_count":90},{"id":"2302.03262","title":"Membership Inference Attacks against Diffusion Models","created":"2023-02-07","cited_by_count":86},{"id":"1811.02054","title":"Exploring Connections Between Active Learning and Model Extraction","created":"2018-11-05","cited_by_count":85},{"id":"2111.10752","title":"Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability","created":"2021-11-21","cited_by_count":84},{"id":"2101.01341","title":"Practical Blind Membership Inference Attack via Differential Comparisons","created":"2021-01-05","cited_by_count":78},{"id":"1812.00910","title":"Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning","created":"2018-12-03","cited_by_count":77},{"id":"1905.11742","title":"Overlearning Reveals Sensitive Attributes","created":"2019-05-28","cited_by_count":76},{"id":"2201.10787","title":"Variational Model Inversion Attacks","created":"2022-01-26","cited_by_count":75},{"id":"1806.05476","title":"Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data","created":"2018-06-14","cited_by_count":70},{"id":"2003.04884","title":"Cryptanalytic Extraction of Neural Network Models","created":"2020-03-10","cited_by_count":68},{"id":"2203.08669","title":"MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients","created":"2022-03-16","cited_by_count":68},{"id":"2003.06468","title":"GeoDA: a geometric framework for black-box adversarial attacks","created":"2020-03-13","cited_by_count":66},{"id":"2302.12580","title":"Membership Inference Attacks against Synthetic Data through Overfitting Detection","created":"2023-02-24","cited_by_count":66},{"id":"2307.03694","title":"Scalable Membership Inference Attacks via Quantile Regression","created":"2023-07-07","cited_by_count":65},{"id":"1507.00677","title":"Distributional Smoothing with Virtual Adversarial Training","created":"2015-07-02","cited_by_count":61},{"id":"2011.12807","title":"SurFree: a fast surrogate-free black-box attack","created":"2020-11-25","cited_by_count":60},{"id":"2101.11073","title":"Property Inference From Poisoning","created":"2021-01-26","cited_by_count":60},{"id":"2201.12179","title":"Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks","created":"2022-01-28","cited_by_count":60},{"id":"2211.09773","title":"T-SEA: Transfer-based Self-Ensemble Attack on Object Detection","created":"2022-11-16","cited_by_count":59},{"id":"2210.10936","title":"FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information","created":"2022-10-20","cited_by_count":58},{"id":"1709.05583","title":"Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification","created":"2017-09-17","cited_by_count":56},{"id":"1809.04127","title":"Poisoning Attacks to Graph-Based Recommender Systems","created":"2018-09-11","cited_by_count":56}],"next_cursor":"100"}