{"list":"other","total":52,"rows":[{"id":"1801.03924","title":"The Unreasonable Effectiveness of Deep Features as a Perceptual Metric","created":"2018-01-11","cited_by_count":11177},{"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":"1903.12261","title":"Benchmarking Neural Network Robustness to Common Corruptions and Perturbations","created":"2019-03-28","cited_by_count":2877},{"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":"1511.04599","title":"DeepFool: a simple and accurate method to fool deep neural networks","created":"2015-11-14","cited_by_count":1318},{"id":"1702.01135","title":"Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks","created":"2017-02-03","cited_by_count":744},{"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":"2010.09670","title":"RobustBench: a standardized adversarial robustness benchmark","created":"2020-10-19","cited_by_count":661},{"id":"1711.00851","title":"Provable defenses against adversarial examples via the convex outer adversarial polytope","created":"2017-11-02","cited_by_count":567},{"id":"1902.06705","title":"On Evaluating Adversarial Robustness","created":"2019-02-18","cited_by_count":447},{"id":"1811.00866","title":"Efficient Neural Network Robustness Certification with General Activation Functions","created":"2018-11-02","cited_by_count":445},{"id":"1907.02044","title":"Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack","created":"2019-07-03","cited_by_count":334},{"id":"1801.09344","title":"Certified Defenses against Adversarial Examples","created":"2018-01-29","cited_by_count":306},{"id":"2102.01356","title":"Recent Advances in Adversarial Training for Adversarial Robustness","created":"2021-02-02","cited_by_count":279},{"id":"1804.09699","title":"Towards Fast Computation of Certified Robustness for ReLU Networks","created":"2018-04-25","cited_by_count":255},{"id":"1801.02612","title":"Spatially Transformed Adversarial Examples","created":"2018-01-08","cited_by_count":195},{"id":"1705.08475","title":"Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation","created":"2017-05-23","cited_by_count":185},{"id":"1801.10578","title":"Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach","created":"2018-01-31","cited_by_count":182},{"id":"1711.08013","title":"OSQP: An Operator Splitting Solver for Quadratic Programs","created":"2017-11-21","cited_by_count":161},{"id":"1805.12514","title":"Scaling provable adversarial defenses","created":"2018-05-31","cited_by_count":143},{"id":"1909.06588","title":"Branch and Bound for Piecewise Linear Neural Network Verification","created":"2019-09-14","cited_by_count":138},{"id":"2006.12655","title":"Perceptual Adversarial Robustness: Defense Against Unseen Threat Models","created":"2020-06-22","cited_by_count":137},{"id":"1906.06032","title":"Adversarial Training Can Hurt Generalization","created":"2019-06-14","cited_by_count":132},{"id":"1902.08722","title":"A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks","created":"2019-02-23","cited_by_count":125},{"id":"1610.00768","title":"Technical Report on the CleverHans v2.1.0 Adversarial Examples Library","created":"2016-10-03","cited_by_count":124},{"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":"1909.05040","title":"Sparse and Imperceivable Adversarial Attacks","created":"2019-09-11","cited_by_count":95},{"id":"1902.07906","title":"Wasserstein Adversarial Examples via Projected Sinkhorn Iterations","created":"2019-02-21","cited_by_count":89},{"id":"1909.04068","title":"Adversarial Robustness Against the Union of Multiple Perturbation Models","created":"2019-09-09","cited_by_count":88},{"id":"1804.00499","title":"Semantic Adversarial Examples","created":"2018-03-16","cited_by_count":86},{"id":"1906.00001","title":"Functional Adversarial Attacks","created":"2019-05-29","cited_by_count":82},{"id":"2103.03638","title":"PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximations","created":"2021-03-05","cited_by_count":77},{"id":"2112.12376","title":"Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization","created":"2021-12-23","cited_by_count":76},{"id":"1511.03034","title":"Learning with a Strong Adversary","created":"2015-11-10","cited_by_count":68},{"id":"2010.11645","title":"Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming","created":"2020-10-22","cited_by_count":66},{"id":"1807.06732","title":"Motivating the Rules of the Game for Adversarial Example Research","created":"2018-07-18","cited_by_count":57},{"id":"2103.01208","title":"Mind the box: $l_1$-APGD for sparse adversarial attacks on image classifiers","created":"2021-03-01","cited_by_count":51},{"id":"1805.10265","title":"Training verified learners with learned verifiers","created":"2018-05-25","cited_by_count":50},{"id":"2102.12827","title":"Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints","created":"2021-02-25","cited_by_count":50},{"id":"1905.11213","title":"Provable robustness against all adversarial $l_p$-perturbations for $p\\geq 1$","created":"2019-05-27","cited_by_count":39},{"id":"1912.00574","title":"Fastened CROWN: Tightened Neural Network Robustness Certificates","created":"2019-12-02","cited_by_count":39},{"id":"2006.08141","title":"Non-convex Min-Max Optimization: Applications, Challenges, and Recent Theoretical Advances","created":"2020-06-15","cited_by_count":38},{"id":"1810.12042","title":"Logit Pairing Methods Can Fool Gradient-Based Attacks","created":"2018-10-29","cited_by_count":27},{"id":"1804.11003","title":"Gradient Sampling Methods for Nonsmooth Optimization","created":"2018-04-29","cited_by_count":23},{"id":"1905.13587","title":"GENO -- GENeric Optimization for Classical Machine Learning","created":"2019-05-31","cited_by_count":12},{"id":"2210.00973","title":"NCVX: A General-Purpose Optimization Solver for Constrained Machine and Deep Learning","created":"2022-10-03","cited_by_count":7},{"id":"2203.16340","title":"Optimization for Classical Machine Learning Problems on the GPU","created":"2022-03-30","cited_by_count":5},{"id":"2206.06496","title":"Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations","created":"2022-06-13","cited_by_count":3},{"id":"2210.00621","title":"Optimization for Robustness Evaluation beyond $\\ell_p$ Metrics","created":"2022-10-02","cited_by_count":3}],"next_cursor":null}