Imperceptible and Sparse Adversarial Attacks via a Dual-Population-Based Constrained Evolutionary Algorithm
暂无分享,去创建一个
Yaochu Jin | Xingyi Zhang | Ye Tian | Shangshang Yang | Jingwen Pan | S. He
[1] Ye Tian,et al. Evolutionary Large-Scale Multi-Objective Optimization: A Survey , 2021, ACM Comput. Surv..
[2] Yong Man Ro,et al. Robust Decision-Based Black-Box Adversarial Attack via Coarse-To-Fine Random Search , 2021, 2021 IEEE International Conference on Image Processing (ICIP).
[3] Wenjian Luo,et al. Hiding All Labels for Multi-label Images: An Empirical Study of Adversarial Examples , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).
[4] Martin Pilát,et al. Beating White-Box Defenses with Black-Box Attacks , 2021, 2021 International Joint Conference on Neural Networks (IJCNN).
[5] Marius Popescu,et al. EvoBA: An Evolution Strategy as a Strong Baseline forBlack-Box Adversarial Attacks , 2021, ICONIP.
[6] Chenwang Wu,et al. Genetic Algorithm with Multiple Fitness Functions for Generating Adversarial Examples , 2021, 2021 IEEE Congress on Evolutionary Computation (CEC).
[7] Yaochu Jin,et al. A Gradient-Guided Evolutionary Approach to Training Deep Neural Networks , 2021, IEEE Transactions on Neural Networks and Learning Systems.
[8] Fabio Roli,et al. Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints , 2021, NeurIPS.
[9] Tao Zhang,et al. A Coevolutionary Framework for Constrained Multiobjective Optimization Problems , 2021, IEEE Transactions on Evolutionary Computation.
[10] Jia Liu,et al. Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks , 2021, Neurocomputing.
[11] Issa M. Khalil,et al. ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples , 2020, IEEE Access.
[12] Sankha Subhra Mullick,et al. A black-box adversarial attack strategy with adjustable sparsity and generalizability for deep image classifiers , 2020, Pattern Recognit..
[13] Nathaniel D. Bastian,et al. Adversarial Machine Learning in Network Intrusion Detection Systems , 2020, Expert Syst. Appl..
[14] Ye Tian,et al. An Evolutionary Algorithm for Large-Scale Sparse Multiobjective Optimization Problems , 2020, IEEE Transactions on Evolutionary Computation.
[15] Matthias Hein,et al. Sparse and Imperceivable Adversarial Attacks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[16] Xuan Wang,et al. A Multi-objective Examples Generation Approach to Fool the Deep Neural Networks in the Black-Box Scenario , 2019, 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC).
[17] Fu Song,et al. Taking Care of the Discretization Problem: A Comprehensive Study of the Discretization Problem and a Black-Box Adversarial Attack in Discrete Integer Domain , 2019, IEEE Transactions on Dependable and Secure Computing.
[18] Wei Liu,et al. Efficient Decision-Based Black-Box Adversarial Attacks on Face Recognition , 2019, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[19] Danilo Vasconcellos Vargas,et al. Understanding the One Pixel Attack: Propagation Maps and Locality Analysis , 2019, AISafety@IJCAI.
[20] Seyed-Mohsen Moosavi-Dezfooli,et al. SparseFool: A Few Pixels Make a Big Difference , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Anqi Xu,et al. Maximal Jacobian-based Saliency Map Attack , 2018, ArXiv.
[22] Deniz Erdogmus,et al. Structured Adversarial Attack: Towards General Implementation and Better Interpretability , 2018, ICLR.
[23] Aleksander Madry,et al. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors , 2018, ICLR.
[24] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[25] Atul Prakash,et al. Robust Physical-World Attacks on Deep Learning Visual Classification , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[26] Logan Engstrom,et al. Black-box Adversarial Attacks with Limited Queries and Information , 2018, ICML.
[27] Jian Shen,et al. Finger vein secure biometric template generation based on deep learning , 2018, Soft Comput..
[28] Martín Abadi,et al. Adversarial Patch , 2017, ArXiv.
[29] Matthias Bethge,et al. Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models , 2017, ICLR.
[30] Kouichi Sakurai,et al. One Pixel Attack for Fooling Deep Neural Networks , 2017, IEEE Transactions on Evolutionary Computation.
[31] Jun Zhu,et al. Boosting Adversarial Attacks with Momentum , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[32] Jinfeng Yi,et al. ZOO: Zeroth Order Optimization Based Black-box Attacks to Deep Neural Networks without Training Substitute Models , 2017, AISec@CCS.
[33] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[34] Jan Hendrik Witte,et al. Deep Learning for Finance: Deep Portfolios , 2016 .
[35] David A. Wagner,et al. Towards Evaluating the Robustness of Neural Networks , 2016, 2017 IEEE Symposium on Security and Privacy (SP).
[36] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[37] Xin Zhang,et al. End to End Learning for Self-Driving Cars , 2016, ArXiv.
[38] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[39] Ananthram Swami,et al. The Limitations of Deep Learning in Adversarial Settings , 2015, 2016 IEEE European Symposium on Security and Privacy (EuroS&P).
[40] Seyed-Mohsen Moosavi-Dezfooli,et al. DeepFool: A Simple and Accurate Method to Fool Deep Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[41] Ye Tian,et al. An Efficient Approach to Nondominated Sorting for Evolutionary Multiobjective Optimization , 2015, IEEE Transactions on Evolutionary Computation.
[42] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[43] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[44] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[45] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.
[46] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[47] Tara N. Sainath,et al. FUNDAMENTAL TECHNOLOGIES IN MODERN SPEECH RECOGNITION Digital Object Identifier 10.1109/MSP.2012.2205597 , 2012 .
[48] Francisco Herrera,et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..
[49] Qingfu Zhang,et al. Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..
[50] P. N. Suganthan,et al. Differential Evolution: A Survey of the State-of-the-Art , 2011, IEEE Transactions on Evolutionary Computation.
[51] R. Lyndon While,et al. A faster algorithm for calculating hypervolume , 2006, IEEE Transactions on Evolutionary Computation.
[52] Kalyanmoy Deb,et al. A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..
[53] Danilo Vasconcellos Vargas,et al. Adversarial Robustness Assessment : Why both L 0 and L ∞ Attacks Are Necessary , 2021 .
[54] Baoyuan Wu,et al. Sparse Adversarial Attack via Perturbation Factorization , 2020, ECCV.
[55] Li. Two-Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization , 2018 .
[56] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[57] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[58] Kalyanmoy Deb,et al. A combined genetic adaptive search (GeneAS) for engineering design , 1996 .
[59] Kalyanmoy Deb,et al. Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..