{"list":"other","total":79,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1409.1556","title":"Very Deep Convolutional Networks for Large-Scale Image Recognition","created":"2014-09-04","cited_by_count":15364},{"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":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"id":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"id":"1503.03832","title":"FaceNet: A Unified Embedding for Face Recognition and Clustering","created":"2015-03-12","cited_by_count":2849},{"id":"1901.08573","title":"Theoretically Principled Trade-off between Robustness and Accuracy","created":"2019-01-24","cited_by_count":1517},{"id":"2003.01690","title":"Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks","created":"2020-03-03","cited_by_count":1451},{"id":"1902.02918","title":"Certified Adversarial Robustness via Randomized Smoothing","created":"2019-02-08","cited_by_count":1340},{"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":"1807.11164","title":"ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design","created":"2018-07-30","cited_by_count":1136},{"id":"1602.07360","title":"SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size","created":"2016-02-24","cited_by_count":1060},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1604.02878","title":"Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks","created":"2016-04-11","cited_by_count":903},{"id":"1511.07528","title":"The Limitations of Deep Learning in Adversarial Settings","created":"2015-11-24","cited_by_count":790},{"id":"2001.03994","title":"Fast is better than free: Revisiting adversarial training","created":"2020-01-12","cited_by_count":727},{"id":"1708.06131","title":"Evasion Attacks against Machine Learning at Test Time","created":"2017-08-21","cited_by_count":689},{"id":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1904.12843","title":"Adversarial Training for Free!","created":"2019-04-29","cited_by_count":643},{"id":"1711.00851","title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","created":"2017-11-02","cited_by_count":567},{"id":"1711.00117","title":"Countering Adversarial Images using Input Transformations","created":"2017-10-31","cited_by_count":520},{"id":"2002.11569","title":"Overfitting in adversarially robust deep learning","created":"2020-02-26","cited_by_count":506},{"id":"1802.03471","title":"Certified Robustness to Adversarial Examples with Differential Privacy","created":"2018-02-09","cited_by_count":468},{"id":"1905.13736","title":"Unlabeled Data Improves Adversarial Robustness","created":"2019-05-31","cited_by_count":416},{"id":"1901.09960","title":"Using Pre-Training Can Improve Model Robustness and Uncertainty","created":"2019-01-28","cited_by_count":373},{"id":"1805.06605","title":"Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models","created":"2018-05-17","cited_by_count":362},{"id":"1904.02144","title":"HopSkipJumpAttack: A Query-Efficient Decision-Based Attack","created":"2019-04-03","cited_by_count":333},{"id":"2007.08489","title":"Do Adversarially Robust ImageNet Models Transfer Better?","created":"2020-07-16","cited_by_count":316},{"id":"1703.00410","title":"Detecting Adversarial Samples from Artifacts","created":"2017-03-01","cited_by_count":245},{"id":"2002.11242","title":"Attacks Which Do Not Kill Training Make Adversarial Learning Stronger","created":"2020-02-26","cited_by_count":243},{"id":"2110.09468","title":"Improving Robustness using Generated Data","created":"2021-10-18","cited_by_count":234},{"id":"1802.05666","title":"Adversarial Risk and the Dangers of Evaluating Against Weak Attacks","created":"2018-02-15","cited_by_count":221},{"id":"2007.02617","title":"Understanding and Improving Fast Adversarial Training","created":"2020-07-06","cited_by_count":214},{"id":"2010.01736","title":"Geometry-aware Instance-reweighted Adversarial Training","created":"2020-10-05","cited_by_count":198},{"id":"1904.13000","title":"Adversarial Training and Robustness for Multiple Perturbations","created":"2019-04-30","cited_by_count":183},{"id":"1904.04433","title":"Efficient Decision-based Black-box Adversarial Attacks on Face Recognition","created":"2019-04-09","cited_by_count":175},{"id":"1812.02637","title":"MMA Training: Direct Input Space Margin Maximization through Adversarial Training","created":"2018-12-06","cited_by_count":172},{"id":"1908.08705","title":"AdvHat: Real-world adversarial attack on ArcFace Face ID system","created":"2019-08-23","cited_by_count":163},{"id":"2003.12862","title":"Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning","created":"2020-03-28","cited_by_count":153},{"id":"1906.02337","title":"MNIST-C: A Robustness Benchmark for Computer Vision","created":"2019-06-05","cited_by_count":138},{"id":"2010.06121","title":"To be Robust or to be Fair: Towards Fairness in Adversarial Training","created":"2020-10-13","cited_by_count":137},{"id":"2002.10319","title":"Self-Adaptive Training: beyond Empirical Risk Minimization","created":"2020-02-24","cited_by_count":126},{"id":"1903.10346","title":"Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition","created":"2019-03-22","cited_by_count":124},{"id":"2104.09425","title":"Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?","created":"2021-04-19","cited_by_count":113},{"id":"2202.10103","title":"Robustness and Accuracy Could Be Reconcilable by (Proper) Definition","created":"2022-02-21","cited_by_count":111},{"id":"1705.02498","title":"A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions","created":"2017-05-06","cited_by_count":108},{"id":"2010.09470","title":"Dos and Don'ts of Machine Learning in Computer Security","created":"2020-10-19","cited_by_count":102},{"id":"2011.11164","title":"Learnable Boundary Guided Adversarial Training","created":"2020-11-23","cited_by_count":93},{"id":"2110.12976","title":"Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks","created":"2021-10-25","cited_by_count":91},{"id":"1808.05665","title":"Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding","created":"2018-08-16","cited_by_count":85},{"id":"1801.00349","title":"A General Framework for Adversarial Examples with Objectives","created":"2017-12-31","cited_by_count":84},{"id":"2110.03825","title":"Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks","created":"2021-10-07","cited_by_count":74},{"id":"2003.09461","title":"Adversarial Robustness on In- and Out-Distribution Improves Explainability","created":"2020-03-20","cited_by_count":73},{"id":"1801.08535","title":"CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition","created":"2018-01-24","cited_by_count":66},{"id":"1909.09552","title":"Defending Against Physically Realizable Attacks on Image Classification","created":"2019-09-20","cited_by_count":66},{"id":"2003.06814","title":"Towards Face Encryption by Generating Adversarial Identity Masks","created":"2020-03-15","cited_by_count":63},{"id":"1511.05122","title":"Adversarial Manipulation of Deep Representations","created":"2015-11-16","cited_by_count":56},{"id":"2006.12621","title":"Fairness Through Robustness: Investigating Robustness Disparity in Deep Learning","created":"2020-06-17","cited_by_count":56},{"id":"2007.06622","title":"SoK: The Faults in our ASRs: An Overview of Attacks against Automatic Speech Recognition and Speaker Identification Systems","created":"2020-07-13","cited_by_count":46},{"id":"1810.11793","title":"Robust Audio Adversarial Example for a Physical Attack","created":"2018-10-28","cited_by_count":44},{"id":"2003.11145","title":"Adversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study","created":"2020-03-24","cited_by_count":40},{"id":"1910.05262","title":"Hear \"No Evil\", See \"Kenansville\": Efficient and Transferable Black-Box Attacks on Speech Recognition and Voice Identification Systems","created":"2019-10-11","cited_by_count":38},{"id":"1904.05734","title":"Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems","created":"2019-03-18","cited_by_count":36},{"id":"2006.03089","title":"Towards Understanding Fast Adversarial Training","created":"2020-06-04","cited_by_count":33},{"id":"2110.15317","title":"Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework","created":"2021-10-28","cited_by_count":28},{"id":"2003.08861","title":"Face-Off: Adversarial Face Obfuscation","created":"2020-03-19","cited_by_count":25},{"id":"2006.11946","title":"Light Commands: Laser-Based Audio Injection Attacks on Voice-Controllable Systems","created":"2020-06-22","cited_by_count":18},{"id":"1703.06857","title":"On the Limitation of Convolutional Neural Networks in Recognizing Negative Images","created":"2017-03-20","cited_by_count":17},{"id":"2112.04558","title":"SoK: Anti-Facial Recognition Technology","created":"2021-12-08","cited_by_count":16},{"id":"2106.02078","title":"Improving Neural Network Robustness via Persistency of Excitation","created":"2021-06-03","cited_by_count":14},{"id":"1802.06806","title":"Divide, Denoise, and Defend against Adversarial Attacks","created":"2018-02-19","cited_by_count":12},{"id":"2010.10242","title":"Ulixes: Facial Recognition Privacy with Adversarial Machine Learning","created":"2020-10-20","cited_by_count":7},{"id":"1810.08640","title":"On Extensions of CLEVER: A Neural Network Robustness Evaluation Algorithm","created":"2018-10-19","cited_by_count":5},{"id":"2112.14232","title":"Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks","created":"2021-12-28","cited_by_count":4}],"next_cursor":null}