{"list":"other","total":146,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"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":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"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":"1610.05820","title":"Membership Inference Attacks against Machine Learning Models","created":"2016-10-18","cited_by_count":2281},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"id":"1607.02533","title":"Adversarial examples in the physical world","created":"2016-07-08","cited_by_count":1769},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"1811.12231","title":"ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness","created":"2018-11-29","cited_by_count":1432},{"id":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1802.00420","title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","created":"2018-02-01","cited_by_count":1268},{"id":"1603.08511","title":"Colorful Image Colorization","created":"2016-03-28","cited_by_count":1114},{"id":"1905.02175","title":"Adversarial Examples Are Not Bugs, They Are Features","created":"2019-05-06","cited_by_count":953},{"id":"1602.02697","title":"Practical Black-Box Attacks against Machine Learning","created":"2016-02-08","cited_by_count":903},{"id":"1705.07204","title":"Ensemble Adversarial Training: Attacks and Defenses","created":"2017-05-19","cited_by_count":840},{"id":"1511.07528","title":"The Limitations of Deep Learning in Adversarial Settings","created":"2015-11-24","cited_by_count":790},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"2012.09688","title":"PCT: Point cloud transformer","created":"2020-12-17","cited_by_count":732},{"id":"1912.00049","title":"Square Attack: a query-efficient black-box adversarial attack via random search","created":"2019-11-29","cited_by_count":685},{"id":"1511.04508","title":"Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks","created":"2015-11-14","cited_by_count":678},{"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":"1803.06978","title":"Improving Transferability of Adversarial Examples with Input Diversity","created":"2018-03-19","cited_by_count":599},{"id":"1710.08864","title":"One pixel attack for fooling deep neural networks","created":"2017-10-24","cited_by_count":582},{"id":"1611.02770","title":"Delving into Transferable Adversarial Examples and Black-box Attacks","created":"2016-11-08","cited_by_count":536},{"id":"1712.09665","title":"Adversarial Patch","created":"2017-12-27","cited_by_count":517},{"id":"1804.08598","title":"Black-box Adversarial Attacks with Limited Queries and Information","created":"2018-04-23","cited_by_count":501},{"id":"2205.07460","title":"Diffusion Models for Adversarial Purification","created":"2022-05-16","cited_by_count":476},{"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":"1605.07277","title":"Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples","created":"2016-05-24","cited_by_count":446},{"id":"1904.02884","title":"Evading Defenses to Transferable Adversarial Examples by Translation-Invariant Attacks","created":"2019-04-05","cited_by_count":442},{"id":"1711.07566","title":"Neural 3D Mesh Renderer","created":"2017-11-20","cited_by_count":437},{"id":"1908.06281","title":"Nesterov Accelerated Gradient and Scale Invariance for Adversarial Attacks","created":"2019-08-17","cited_by_count":355},{"id":"2004.06660","title":"Weight Poisoning Attacks on Pre-trained Models","created":"2020-04-14","cited_by_count":341},{"id":"1904.02144","title":"HopSkipJumpAttack: A Query-Efficient Decision-Based Attack","created":"2019-04-03","cited_by_count":333},{"id":"1703.08603","title":"Adversarial Examples for Semantic Segmentation and Object Detection","created":"2017-03-24","cited_by_count":275},{"id":"1801.02610","title":"Generating Adversarial Examples with Adversarial Networks","created":"2018-01-08","cited_by_count":258},{"id":"1905.07121","title":"Simple Black-box Adversarial Attacks","created":"2019-05-17","cited_by_count":251},{"id":"2103.15571","title":"Enhancing the Transferability of Adversarial Attacks through Variance Tuning","created":"2021-03-29","cited_by_count":244},{"id":"1801.02612","title":"Spatially Transformed Adversarial Examples","created":"2018-01-08","cited_by_count":195},{"id":"1710.11342","title":"Generating Natural Adversarial Examples","created":"2017-10-31","cited_by_count":183},{"id":"1804.05810","title":"ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector","created":"2018-04-16","cited_by_count":159},{"id":"1704.03453","title":"The Space of Transferable Adversarial Examples","created":"2017-04-11","cited_by_count":158},{"id":"1709.04114","title":"EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples","created":"2017-09-13","cited_by_count":155},{"id":"1712.02328","title":"Generative Adversarial Perturbations","created":"2017-12-06","cited_by_count":153},{"id":"1907.06826","title":"Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving","created":"2019-07-16","cited_by_count":152},{"id":"1807.07978","title":"Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors","created":"2018-07-20","cited_by_count":146},{"id":"1907.10823","title":"Enhancing Adversarial Example Transferability with an Intermediate Level Attack","created":"2019-07-23","cited_by_count":140},{"id":"1805.11770","title":"AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks","created":"2018-05-30","cited_by_count":137},{"id":"2004.00543","title":"Physically Realizable Adversarial Examples for LiDAR Object Detection","created":"2020-04-01","cited_by_count":137},{"id":"1805.07894","title":"Constructing Unrestricted Adversarial Examples with Generative Models","created":"2018-05-21","cited_by_count":135},{"id":"1807.04457","title":"Query-Efficient Hard-label Black-box Attack:An Optimization-based Approach","created":"2018-07-12","cited_by_count":130},{"id":"1712.05245","title":"Pointwise Convolutional Neural Networks","created":"2017-12-14","cited_by_count":127},{"id":"2103.01050","title":"Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World","created":"2021-03-01","cited_by_count":127},{"id":"2107.14185","title":"Feature Importance-aware Transferable Adversarial Attacks","created":"2021-07-29","cited_by_count":127},{"id":"2003.08757","title":"Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles","created":"2020-03-08","cited_by_count":126},{"id":"1904.01067","title":"Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning","created":"2019-04-01","cited_by_count":122},{"id":"1906.06919","title":"Improving Black-box Adversarial Attacks with a Transfer-based Prior","created":"2019-06-17","cited_by_count":121},{"id":"1909.10773","title":"Sign-OPT: A Query-Efficient Hard-label Adversarial Attack","created":"2019-09-24","cited_by_count":120},{"id":"1812.01687","title":"PointCloud Saliency Maps","created":"2018-11-28","cited_by_count":112},{"id":"1811.09600","title":"Decoupling Direction and Norm for Efficient Gradient-Based L2 Adversarial Attacks and Defenses","created":"2018-11-23","cited_by_count":109},{"id":"2105.03162","title":"Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition","created":"2021-05-07","cited_by_count":97},{"id":"2006.12792","title":"RayS: A Ray Searching Method for Hard-label Adversarial Attack","created":"2020-06-23","cited_by_count":96},{"id":"1909.05040","title":"Sparse and Imperceivable Adversarial Attacks","created":"2019-09-11","cited_by_count":95},{"id":"2006.16974","title":"Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures","created":"2020-06-30","cited_by_count":95},{"id":"1811.02248","title":"SparseFool: a few pixels make a big difference","created":"2018-11-06","cited_by_count":94},{"id":"1905.00441","title":"NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks","created":"2019-05-01","cited_by_count":94},{"id":"1906.07927","title":"SemanticAdv: Generating Adversarial Examples via Attribute-conditional Image Editing","created":"2019-06-19","cited_by_count":93},{"id":"2204.00008","title":"Improving Adversarial Transferability via Neuron Attribution-Based Attacks","created":"2022-03-31","cited_by_count":93},{"id":"1901.03006","title":"Extending Adversarial Attacks and Defenses to Deep 3D Point Cloud Classifiers","created":"2019-01-10","cited_by_count":92},{"id":"2201.05057","title":"On Adversarial Robustness of Trajectory Prediction for Autonomous Vehicles","created":"2022-01-13","cited_by_count":90},{"id":"1902.07906","title":"Wasserstein Adversarial Examples via Projected Sinkhorn Iterations","created":"2019-02-21","cited_by_count":89},{"id":"1911.10891","title":"ColorFool: Semantic Adversarial Colorization","created":"2019-11-25","cited_by_count":89},{"id":"2012.11207","title":"On Success and Simplicity: A Second Look at Transferable Targeted Attacks","created":"2020-12-21","cited_by_count":89},{"id":"1801.08092","title":"Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations","created":"2018-01-24","cited_by_count":88},{"id":"1804.00499","title":"Semantic Adversarial Examples","created":"2018-03-16","cited_by_count":86},{"id":"1801.00349","title":"A General Framework for Adversarial Examples with Objectives","created":"2017-12-31","cited_by_count":84},{"id":"1904.06347","title":"Unrestricted Adversarial Examples via Semantic Manipulation","created":"2019-04-12","cited_by_count":84},{"id":"1912.00461","title":"AdvPC: Transferable Adversarial Perturbations on 3D Point Clouds","created":"2019-12-01","cited_by_count":84},{"id":"1906.00001","title":"Functional Adversarial Attacks","created":"2019-05-29","cited_by_count":82},{"id":"1712.02779","title":"Exploring the Landscape of Spatial Robustness","created":"2017-12-07","cited_by_count":81},{"id":"2106.15058","title":"Improving Transferability of Adversarial Patches on Face Recognition with Generative Models","created":"2021-06-29","cited_by_count":80},{"id":"2203.05151","title":"Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity","created":"2022-03-10","cited_by_count":77},{"id":"2303.16378","title":"A Pilot Study of Query-Free Adversarial Attack against Stable Diffusion","created":"2023-03-29","cited_by_count":76},{"id":"2109.07193","title":"FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack","created":"2021-09-15","cited_by_count":74},{"id":"1911.02466","title":"Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distance","created":"2019-11-06","cited_by_count":73},{"id":"2305.10665","title":"Content-based Unrestricted Adversarial Attack","created":"2023-05-18","cited_by_count":73},{"id":"1711.07183","title":"Adversarial Attacks Beyond the Image Space","created":"2017-11-20","cited_by_count":67},{"id":"2003.06468","title":"GeoDA: a geometric framework for black-box adversarial attacks","created":"2020-03-13","cited_by_count":66},{"id":"2006.12834","title":"Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks","created":"2020-06-23","cited_by_count":65},{"id":"2009.06701","title":"Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack","created":"2020-09-14","cited_by_count":64},{"id":"1901.04684","title":"The Limitations of Adversarial Training and the Blind-Spot Attack","created":"2019-01-15","cited_by_count":63},{"id":"1907.05418","title":"Adversarial Objects Against LiDAR-Based Autonomous Driving Systems","created":"2019-07-11","cited_by_count":63},{"id":"2111.00898","title":"Availability Attacks Create Shortcuts","created":"2021-11-01","cited_by_count":62},{"id":"2011.12807","title":"SurFree: a fast surrogate-free black-box attack","created":"2020-11-25","cited_by_count":60},{"id":"2007.06189","title":"Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations","created":"2020-07-13","cited_by_count":59},{"id":"2009.03728","title":"Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective","created":"2020-09-08","cited_by_count":59},{"id":"1807.06732","title":"Motivating the Rules of the Game for Adversarial Example Research","created":"2018-07-18","cited_by_count":57}],"next_cursor":"100"}