{"list":"other","total":57,"rows":[{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"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":"1608.04644","title":"Towards Evaluating the Robustness of Neural Networks","created":"2016-08-16","cited_by_count":2998},{"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":"1709.00029","title":"EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification","created":"2017-08-31","cited_by_count":1496},{"id":"1604.07316","title":"End to End Learning for Self-Driving Cars","created":"2016-04-25","cited_by_count":1009},{"id":"1611.01236","title":"Adversarial Machine Learning at Scale","created":"2016-11-04","cited_by_count":936},{"id":"1707.02968","title":"Revisiting Unreasonable Effectiveness of Data in Deep Learning Era","created":"2017-07-10","cited_by_count":919},{"id":"1610.08401","title":"Universal adversarial perturbations","created":"2016-10-26","cited_by_count":784},{"id":"2001.03994","title":"Fast is better than free: Revisiting adversarial training","created":"2020-01-12","cited_by_count":727},{"id":"1908.07125","title":"Universal Adversarial Triggers for Attacking and Analyzing NLP","created":"2019-08-20","cited_by_count":722},{"id":"1511.04508","title":"Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks","created":"2015-11-14","cited_by_count":678},{"id":"2205.07460","title":"Diffusion Models for Adversarial Purification","created":"2022-05-16","cited_by_count":476},{"id":"1711.01991","title":"Mitigating Adversarial Effects Through Randomization","created":"2017-11-06","cited_by_count":383},{"id":"1712.02976","title":"Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser","created":"2017-12-08","cited_by_count":329},{"id":"1812.03411","title":"Feature Denoising for Improving Adversarial Robustness","created":"2018-12-09","cited_by_count":314},{"id":"1705.06640","title":"DeepXplore: Automated Whitebox Testing of Deep Learning Systems","created":"2017-05-18","cited_by_count":311},{"id":"2110.09468","title":"Improving Robustness using Generated Data","created":"2021-10-18","cited_by_count":234},{"id":"1904.04433","title":"Efficient Decision-based Black-box Adversarial Attacks on Face Recognition","created":"2019-04-09","cited_by_count":175},{"id":"1807.07769","title":"Physical Adversarial Examples for Object Detectors","created":"2018-07-20","cited_by_count":163},{"id":"1712.02328","title":"Generative Adversarial Perturbations","created":"2017-12-06","cited_by_count":153},{"id":"1803.07519","title":"DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems","created":"2018-03-20","cited_by_count":147},{"id":"2107.14185","title":"Feature Importance-aware Transferable Adversarial Attacks","created":"2021-07-29","cited_by_count":127},{"id":"2002.10319","title":"Self-Adaptive Training: beyond Empirical Risk Minimization","created":"2020-02-24","cited_by_count":126},{"id":"1808.08444","title":"Guiding Deep Learning System Testing using Surprise Adequacy","created":"2018-08-25","cited_by_count":124},{"id":"1801.02608","title":"LaVAN: Localized and Visible Adversarial Noise","created":"2018-01-08","cited_by_count":113},{"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":"2204.00008","title":"Improving Adversarial Transferability via Neuron Attribution-Based Attacks","created":"2022-03-31","cited_by_count":93},{"id":"1704.05712","title":"Universal Adversarial Perturbations Against Semantic Image Segmentation","created":"2017-04-19","cited_by_count":92},{"id":"1811.11304","title":"Universal Adversarial Training","created":"2018-11-27","cited_by_count":87},{"id":"1911.10695","title":"When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks","created":"2019-11-25","cited_by_count":87},{"id":"2004.05790","title":"Towards Transferable Adversarial Attack against Deep Face Recognition","created":"2020-04-13","cited_by_count":80},{"id":"1905.11911","title":"Controlling Neural Level Sets","created":"2019-05-28","cited_by_count":76},{"id":"1707.05572","title":"Fast Feature Fool: A data independent approach to universal adversarial perturbations","created":"2017-07-18","cited_by_count":66},{"id":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66},{"id":"2103.14641","title":"On Generating Transferable Targeted Perturbations","created":"2021-03-26","cited_by_count":65},{"id":"2007.06189","title":"Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations","created":"2020-07-13","cited_by_count":59},{"id":"1909.04385","title":"FDA: Feature Disruptive Attack","created":"2019-09-10","cited_by_count":56},{"id":"1712.03390","title":"NAG: Network for Adversary Generation","created":"2017-12-09","cited_by_count":51},{"id":"2106.06804","title":"Entropy-based Logic Explanations of Neural Networks","created":"2021-06-12","cited_by_count":51},{"id":"1709.03582","title":"Art of singular vectors and universal adversarial perturbations","created":"2017-09-11","cited_by_count":49},{"id":"1711.05929","title":"Defense against Universal Adversarial Perturbations","created":"2017-11-16","cited_by_count":48},{"id":"2212.05630","title":"DISCO: Adversarial Defense with Local Implicit Functions","created":"2022-12-11","cited_by_count":34},{"id":"2010.01278","title":"Efficient Robust Training via Backward Smoothing","created":"2020-10-03","cited_by_count":29},{"id":"2010.03300","title":"CD-UAP: Class Discriminative Universal Adversarial Perturbation","created":"2020-10-07","cited_by_count":27},{"id":"1812.03705","title":"Defending Against Universal Perturbations With Shared Adversarial Training","created":"2018-12-10","cited_by_count":25},{"id":"2010.03288","title":"Double Targeted Universal Adversarial Perturbations","created":"2020-10-07","cited_by_count":23},{"id":"2308.06015","title":"Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregation","created":"2023-08-11","cited_by_count":22},{"id":"2111.10759","title":"Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model","created":"2021-11-21","cited_by_count":19},{"id":"2104.03000","title":"Universal Adversarial Training with Class-Wise Perturbations","created":"2021-04-07","cited_by_count":13},{"id":"2111.02331","title":"LTD: Low Temperature Distillation for Gradient Masking-free Adversarial Training","created":"2021-11-03","cited_by_count":13},{"id":"2110.00708","title":"Universal Adversarial Spoofing Attacks against Face Recognition","created":"2021-10-02","cited_by_count":5},{"id":"2306.11261","title":"Comparative Evaluation of Recent Universal Adversarial Perturbations in Image Classification","created":"2023-06-20","cited_by_count":4}],"next_cursor":null}