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Han, K.[Keji],
Chen, H.[Hui],
Li, Y.[Yun],
Ensemble adversarial black-box attacks against deep learning systems,
PR(101), 2020, pp. 107184.
Elsevier DOI
2003
Black-box attack, Vulnerability, Ensemble adversarial attack,
Diversity, Transferability
BibRef
Correia-Silva, J.R.[Jacson Rodrigues],
Berriel, R.F.[Rodrigo F.],
Badue, C.[Claudine],
de Souza, A.F.[Alberto F.],
Oliveira-Santos, T.[Thiago],
Copycat CNN: Are random non-Labeled data enough to steal knowledge
from black-box models?,
PR(113), 2021, pp. 107830.
Elsevier DOI
2103
Copy a CNN model.
Deep learning, Convolutional neural network,
Neural network attack, Stealing network knowledge, Knowledge distillation
BibRef
Gragnaniello, D.[Diego],
Marra, F.[Francesco],
Verdoliva, L.[Luisa],
Poggi, G.[Giovanni],
Perceptual quality-preserving black-box attack against deep learning
image classifiers,
PRL(147), 2021, pp. 142-149.
Elsevier DOI
2106
Image classification, Face recognition, Adversarial attacks, Black-box
BibRef
Li, N.N.[Nan-Nan],
Chen, Z.Z.[Zhen-Zhong],
Toward Visual Distortion in Black-Box Attacks,
IP(30), 2021, pp. 6156-6167.
IEEE DOI
2107
Distortion, Visualization, Measurement, Loss measurement,
Optimization, Convergence, Training, Black-box attack, classification
BibRef
Cinà, A.E.[Antonio Emanuele],
Torcinovich, A.[Alessandro],
Pelillo, M.[Marcello],
A black-box adversarial attack for poisoning clustering,
PR(122), 2022, pp. 108306.
Elsevier DOI
2112
Adversarial learning, Unsupervised learning, Clustering,
Robustness evaluation, Machine learning security
BibRef
Ghosh, A.[Arka],
Mullick, S.S.[Sankha Subhra],
Datta, S.[Shounak],
Das, S.[Swagatam],
Das, A.K.[Asit Kr.],
Mallipeddi, R.[Rammohan],
A black-box adversarial attack strategy with adjustable sparsity and
generalizability for deep image classifiers,
PR(122), 2022, pp. 108279.
Elsevier DOI
2112
Adversarial attack, Black-box attack,
Convolutional image classifier, Differential evolution,
Sparse universal attack
BibRef
Chen, S.[Sizhe],
He, F.[Fan],
Huang, X.L.[Xiao-Lin],
Zhang, K.[Kun],
Relevance attack on detectors,
PR(124), 2022, pp. 108491.
Elsevier DOI
2203
Adversarial attack, Attack transferability, Black-box attack,
Relevance map, Interpreters, Object detection
BibRef
Wei, X.X.[Xing-Xing],
Yan, H.Q.[Huan-Qian],
Li, B.[Bo],
Sparse Black-Box Video Attack with Reinforcement Learning,
IJCV(130), No. 6, June 2022, pp. 1459-1473.
Springer DOI
2207
BibRef
Hu, Z.C.[Zi-Chao],
Li, H.[Heng],
Yuan, L.H.[Li-Heng],
Cheng, Z.[Zhang],
Yuan, W.[Wei],
Zhu, M.[Ming],
Model scheduling and sample selection for ensemble adversarial
example attacks,
PR(130), 2022, pp. 108824.
Elsevier DOI
2206
Adversarial example, Black-box attack, Model scheduling, Sample selection
BibRef
Huang, L.F.[Li-Feng],
Wei, S.X.[Shu-Xin],
Gao, C.Y.[Cheng-Ying],
Liu, N.[Ning],
Cyclical Adversarial Attack Pierces Black-box Deep Neural Networks,
PR(131), 2022, pp. 108831.
Elsevier DOI
2208
Adversarial example, Transferability, Black-box attack, Defenses
BibRef
Peng, B.[Bowen],
Peng, B.[Bo],
Yong, S.W.[Shao-Wei],
Liu, L.[Li],
An Empirical Study of Fully Black-Box and Universal Adversarial
Attack for SAR Target Recognition,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
Li, C.[Chao],
Yao, W.[Wen],
Wang, H.D.[Han-Ding],
Jiang, T.S.[Ting-Song],
Adaptive momentum variance for attention-guided sparse adversarial
attacks,
PR(133), 2023, pp. 108979.
Elsevier DOI
2210
Deep neural networks, Black-box adversarial attacks,
Transferability, Momentum variances
BibRef
Li, T.[Tengjiao],
Li, M.[Maosen],
Yang, Y.H.[Yan-Hua],
Deng, C.[Cheng],
Frequency domain regularization for iterative adversarial attacks,
PR(134), 2023, pp. 109075.
Elsevier DOI
2212
Adversarial examples, Transfer-based attack, Black-box attack,
Frequency-domain characteristics
BibRef
Dong, Y.P.[Yin-Peng],
Cheng, S.Y.[Shu-Yu],
Pang, T.Y.[Tian-Yu],
Su, H.[Hang],
Zhu, J.[Jun],
Query-Efficient Black-Box Adversarial Attacks Guided by a
Transfer-Based Prior,
PAMI(44), No. 12, December 2022, pp. 9536-9548.
IEEE DOI
2212
Estimation, Optimization, Analytical models, Numerical models,
Deep learning, Approximation algorithms, Weight measurement,
transferability
BibRef
Hu, C.[Cong],
Xu, H.Q.[Hao-Qi],
Wu, X.J.[Xiao-Jun],
Substitute Meta-Learning for Black-Box Adversarial Attack,
SPLetters(29), 2022, pp. 2472-2476.
IEEE DOI
2212
Training, Closed box, Task analysis, Signal processing algorithms,
Generators, Classification algorithms, Data models, substitute training
BibRef
Theagarajan, R.[Rajkumar],
Bhanu, B.[Bir],
Privacy Preserving Defense For Black Box Classifiers Against On-Line
Adversarial Attacks,
PAMI(44), No. 12, December 2022, pp. 9503-9520.
IEEE DOI
2212
Training, Perturbation methods, Bayes methods, Uncertainty,
Deep learning, Privacy, Data models, Adversarial defense,
privacy preserving defense
BibRef
Hu, C.Y.[Cheng-Yin],
Shi, W.W.[Wei-Wen],
Tian, L.[Ling],
Li, W.[Wen],
Adversarial Neon Beam: A light-based physical attack to DNNs,
CVIU(238), 2024, pp. 103877.
Elsevier DOI Code:
WWW Link.
2312
DNNs, Black-box light-based physical attack, AdvNB,
Effectiveness, Stealthiness, Robustness
BibRef
Hu, C.Y.[Cheng-Yin],
Shi, W.W.[Wei-Wen],
Tian, L.[Ling],
Adversarial color projection: A projector-based physical-world attack
to DNNs,
IVC(140), 2023, pp. 104861.
Elsevier DOI Code:
WWW Link.
2312
DNNs, Black-box projector-based physical attack,
Adversarial color projection, Effectiveness, Stealthiness, Robustness
BibRef
Shi, Y.C.[Yu-Cheng],
Han, Y.H.[Ya-Hong],
Hu, Q.H.[Qing-Hua],
Yang, Y.[Yi],
Tian, Q.[Qi],
Query-Efficient Black-Box Adversarial Attack With Customized
Iteration and Sampling,
PAMI(45), No. 2, February 2023, pp. 2226-2245.
IEEE DOI
2301
Adaptation models, Optimization, Data models, Computational modeling,
Gaussian noise, Trajectory, transfer-based attack
BibRef
Wei, X.X.[Xing-Xing],
Guo, Y.[Ying],
Yu, J.[Jie],
Zhang, B.[Bo],
Simultaneously Optimizing Perturbations and Positions for Black-Box
Adversarial Patch Attacks,
PAMI(45), No. 7, July 2023, pp. 9041-9054.
IEEE DOI
2306
Perturbation methods, Face recognition, Task analysis,
Optimization, Closed box, Estimation, Detectors, Adversarial patches,
traffic sign recognition
BibRef
Zhang, Y.[Yu],
Gong, Z.Q.[Zhi-Qiang],
Zhang, Y.C.[Yi-Chuang],
Bin, K.C.[Kang-Cheng],
Li, Y.Q.[Yong-Qian],
Qi, J.H.[Jia-Hao],
Wen, H.[Hao],
Zhong, P.[Ping],
Boosting transferability of physical attack against detectors by
redistributing separable attention,
PR(138), 2023, pp. 109435.
Elsevier DOI
2303
Physical attack, Transferability, Multi-layer attention,
Object detection, Black-box models
BibRef
Yin, F.[Fei],
Zhang, Y.[Yong],
Wu, B.Y.[Bao-Yuan],
Feng, Y.[Yan],
Zhang, J.Y.[Jing-Yi],
Fan, Y.B.[Yan-Bo],
Yang, Y.J.[Yu-Jiu],
Generalizable Black-Box Adversarial Attack With Meta Learning,
PAMI(46), No. 3, March 2024, pp. 1804-1818.
IEEE DOI Code:
WWW Link.
2402
Perturbation methods, Closed box, Generators, Task analysis,
Glass box, Training, Adaptation models,
conditional distribution of perturbation
BibRef
Feng, Y.[Yan],
Wu, B.Y.[Bao-Yuan],
Fan, Y.B.[Yan-Bo],
Liu, L.[Li],
Li, Z.F.[Zhi-Feng],
Xia, S.T.[Shu-Tao],
Boosting Black-Box Attack with Partially Transferred Conditional
Adversarial Distribution,
CVPR22(15074-15083)
IEEE DOI
2210
Training, Learning systems, Deep learning, Solid modeling,
Perturbation methods, Computational modeling, Adversarial attack and defense
BibRef
Lu, Y.T.[Yan-Tao],
Ren, H.N.[Hai-Ning],
Chai, W.H.[Wei-Heng],
Velipasalar, S.[Senem],
Li, Y.[Yilan],
Time-aware and task-transferable adversarial attack for perception of
autonomous vehicles,
PRL(178), 2024, pp. 145-152.
Elsevier DOI
2402
Adversarial attack, Black-box, Perception, Real-time
BibRef
Baia, A.E.[Alina Elena],
Poggioni, V.[Valentina],
Cavallaro, A.[Andrea],
Black-Box Attacks on Image Activity Prediction and its Natural
Language Explanations,
AROW23(3688-3697)
IEEE DOI
2401
BibRef
Zhang, Y.H.[Yi-Hua],
Cai, R.[Ruisi],
Chen, T.L.[Tian-Long],
Reza, M.F.[Md Farhamdur],
Rahmati, A.[Ali],
Wu, T.F.[Tian-Fu],
Dai, H.[Huaiyu],
CGBA: Curvature-aware Geometric Black-box Attack,
ICCV23(124-133)
IEEE DOI Code:
WWW Link.
2401
BibRef
Jiang, K.X.[Kai-Xun],
Chen, Z.Y.[Zhao-Yu],
Huang, H.[Hao],
Wang, J.[Jiafeng],
Yang, D.[Dingkang],
Li, B.[Bo],
Wang, Y.[Yan],
Zhang, W.Q.[Wen-Qiang],
Efficient Decision-based Black-box Patch Attacks on Video Recognition,
ICCV23(4356-4366)
IEEE DOI
2401
BibRef
Park, H.[Hojin],
Park, J.[Jaewoo],
Dong, X.[Xingbo],
Teoh, A.B.J.[Andrew Beng Jin],
Towards Query Efficient and Generalizable Black-Box Face
Reconstruction Attack,
ICIP23(1060-1064)
IEEE DOI
2312
BibRef
Han, G.J.[Gyo-Jin],
Choi, J.[Jaehyun],
Lee, H.[Haeil],
Kim, J.[Junmo],
Reinforcement Learning-Based Black-Box Model Inversion Attacks,
CVPR23(20504-20513)
IEEE DOI
2309
BibRef
Williams, P.N.[Phoenix Neale],
Li, K.[Ke],
Black-Box Sparse Adversarial Attack via Multi-Objective Optimisation
CVPR Proceedings,
CVPR23(12291-12301)
IEEE DOI
2309
BibRef
Zhao, A.[Anqi],
Chu, T.[Tong],
Liu, Y.[Yahao],
Li, W.[Wen],
Li, J.J.[Jing-Jing],
Duan, L.X.[Li-Xin],
Minimizing Maximum Model Discrepancy for Transferable Black-box
Targeted Attacks,
CVPR23(8153-8162)
IEEE DOI
2309
BibRef
Cai, Z.[Zikui],
Tan, Y.[Yaoteng],
Asif, M.S.[M. Salman],
Ensemble-based Blackbox Attacks on Dense Prediction,
CVPR23(4045-4055)
IEEE DOI
2309
BibRef
Zhang, C.N.[Chao-Ning],
Benz, P.[Philipp],
Karjauv, A.[Adil],
Cho, J.W.[Jae Won],
Zhang, K.[Kang],
Kweon, I.S.[In So],
Investigating Top-k White-Box and Transferable Black-box Attack,
CVPR22(15064-15073)
IEEE DOI
2210
Measurement, Codes, Semantics, Pattern recognition,
Adversarial attack and defense
BibRef
Wang, B.H.[Bing-Hui],
Li, Y.Q.[You-Qi],
Zhou, P.[Pan],
Bandits for Structure Perturbation-based Black-box Attacks to Graph
Neural Networks with Theoretical Guarantees,
CVPR22(13369-13377)
IEEE DOI
2210
Bridges, Perturbation methods, Computational modeling,
Graph neural networks, Pattern recognition, Task analysis, Adversarial attack and defense
BibRef
Aithal, M.B.[Manjushree B.],
Li, X.H.[Xiao-Hua],
Boundary Defense Against Black-box Adversarial Attacks,
ICPR22(2349-2356)
IEEE DOI
2212
Degradation, Limiting, Gaussian noise, Neural networks, Closed box,
Reliability theory
BibRef
Ji, Y.[Yimu],
Ding, J.Y.[Jian-Yu],
Chen, Z.[Zhiyu],
Wu, F.[Fei],
Zhang, C.[Chi],
Sun, Y.M.[Yi-Ming],
Sun, J.[Jing],
Liu, S.D.[Shang-Dong],
Simulator Attack+ for Black-Box Adversarial Attack,
ICIP22(636-640)
IEEE DOI
2211
Deep learning, Codes, Perturbation methods, Neural networks,
Usability, Meta-learning, Adversarial Attack, Black-box Attack
BibRef
Liang, S.Y.[Si-Yuan],
Li, L.K.[Long-Kang],
Fan, Y.B.[Yan-Bo],
Jia, X.J.[Xiao-Jun],
Li, J.Z.[Jing-Zhi],
Wu, B.Y.[Bao-Yuan],
Cao, X.C.[Xiao-Chun],
A Large-Scale Multiple-Objective Method for Black-box Attack Against
Object Detection,
ECCV22(IV:619-636).
Springer DOI
2211
BibRef
Wang, D.[Dan],
Wang, Y.G.[Yuan-Gen],
Decision-based Black-box Attack Specific to Large-size Images,
ACCV22(II:357-372).
Springer DOI
2307
BibRef
Na, D.B.[Dong-Bin],
Ji, S.[Sangwoo],
Kim, J.[Jong],
Unrestricted Black-box Adversarial Attack Using GAN with Limited
Queries,
AdvRob22(467-482).
Springer DOI
2304
BibRef
Kim, W.J.[Woo Jae],
Hong, S.[Seunghoon],
Yoon, S.E.[Sung-Eui],
Diverse Generative Perturbations on Attention Space for Transferable
Adversarial Attacks,
ICIP22(281-285)
IEEE DOI
2211
Codes, Perturbation methods, Stochastic processes, Generators,
Space exploration, Adversarial examples, Black-box, Diversity
BibRef
Wang, Y.X.[Yi-Xu],
Li, J.[Jie],
Liu, H.[Hong],
Wang, Y.[Yan],
Wu, Y.J.[Yong-Jian],
Huang, F.Y.[Fei-Yue],
Ji, R.R.[Rong-Rong],
Black-Box Dissector:
Towards Erasing-Based Hard-Label Model Stealing Attack,
ECCV22(V:192-208).
Springer DOI
2211
BibRef
Tran, H.[Hoang],
Lu, D.[Dan],
Zhang, G.[Guannan],
Exploiting the Local Parabolic Landscapes of Adversarial Losses to
Accelerate Black-Box Adversarial Attack,
ECCV22(V:317-334).
Springer DOI
2211
BibRef
Wang, T.[Tong],
Yao, Y.[Yuan],
Xu, F.[Feng],
An, S.W.[Sheng-Wei],
Tong, H.H.[Hang-Hang],
Wang, T.[Ting],
An Invisible Black-Box Backdoor Attack Through Frequency Domain,
ECCV22(XIII:396-413).
Springer DOI
2211
BibRef
Sun, X.X.[Xu-Xiang],
Cheng, G.[Gong],
Li, H.[Hongda],
Pei, L.[Lei],
Han, J.W.[Jun-Wei],
Exploring Effective Data for Surrogate Training Towards Black-box
Attack,
CVPR22(15334-15343)
IEEE DOI
2210
Training, Codes, Computational modeling, Semantics, Training data,
Diversity methods, Adversarial attack and defense, retrieval
BibRef
Zhou, L.J.[Lin-Jun],
Cui, P.[Peng],
Zhang, X.X.[Xing-Xuan],
Jiang, Y.[Yinan],
Yang, S.Q.[Shi-Qiang],
Adversarial Eigen Attack on BlackBox Models,
CVPR22(15233-15241)
IEEE DOI
2210
Jacobian matrices, Deep learning, Perturbation methods,
Computational modeling, Training data, Data models, Optimization methods
BibRef
Zhang, J.[Jie],
Li, B.[Bo],
Xu, J.H.[Jiang-He],
Wu, S.[Shuang],
Ding, S.H.[Shou-Hong],
Zhang, L.[Lei],
Wu, C.[Chao],
Towards Efficient Data Free Blackbox Adversarial Attack,
CVPR22(15094-15104)
IEEE DOI
2210
Data privacy, Computational modeling, Training data,
Machine learning, Generative adversarial networks, Data models,
Adversarial attack and defense
BibRef
Wang, W.X.[Wen-Xuan],
Qian, X.L.[Xue-Lin],
Fu, Y.W.[Yan-Wei],
Xue, X.Y.[Xiang-Yang],
DST: Dynamic Substitute Training for Data-free Black-box Attack,
CVPR22(14341-14350)
IEEE DOI
2210
Training, Adaptation models, Computational modeling,
Neural networks, Training data, Logic gates,
Adversarial attack and defense
BibRef
Wang, W.X.[Wen-Xuan],
Yin, B.J.[Bang-Jie],
Yao, T.P.[Tai-Ping],
Zhang, L.[Li],
Fu, Y.W.[Yan-Wei],
Ding, S.H.[Shou-Hong],
Li, J.L.[Ji-Lin],
Huang, F.Y.[Fei-Yue],
Xue, X.Y.[Xiang-Yang],
Delving into Data: Effectively Substitute Training for Black-box
Attack,
CVPR21(4759-4768)
IEEE DOI
2111
Training, Computational modeling, Training data,
Distributed databases, Data visualization, Data models
BibRef
Jia, S.[Shuai],
Song, Y.B.[Yi-Bing],
Ma, C.[Chao],
Yang, X.K.[Xiao-Kang],
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack
for Visual Object Tracking,
CVPR21(6705-6714)
IEEE DOI
2111
Deep learning, Visualization, Correlation, Codes,
Perturbation methods, Robustness
BibRef
Ma, C.[Chen],
Chen, L.[Li],
Yong, J.H.[Jun-Hai],
Simulating Unknown Target Models for Query-Efficient Black-box
Attacks,
CVPR21(11830-11839)
IEEE DOI
2111
Training, Deep learning, Codes,
Computational modeling, Training data, Complexity theory
BibRef
Maho, T.[Thibault],
Furon, T.[Teddy],
Le Merrer, E.[Erwan],
SurFree: a fast surrogate-free black-box attack,
CVPR21(10425-10434)
IEEE DOI
2111
Estimation, Focusing, Machine learning, Distortion,
Pattern recognition, Convergence
BibRef
Li, J.[Jie],
Ji, R.R.[Rong-Rong],
Chen, P.X.[Pei-Xian],
Zhang, B.C.[Bao-Chang],
Hong, X.P.[Xiao-Peng],
Zhang, R.X.[Rui-Xin],
Li, S.X.[Shao-Xin],
Li, J.L.[Ji-Lin],
Huang, F.Y.[Fei-Yue],
Wu, Y.J.[Yong-Jian],
Aha! Adaptive History-driven Attack for Decision-based Black-box
Models,
ICCV21(16148-16157)
IEEE DOI
2203
Dimensionality reduction, Adaptation models,
Perturbation methods, Computational modeling, Optimization, Faces,
BibRef
Zhang, C.N.[Chao-Ning],
Benz, P.[Philipp],
Karjauv, A.[Adil],
Kweon, I.S.[In So],
Data-free Universal Adversarial Perturbation and Black-box Attack,
ICCV21(7848-7857)
IEEE DOI
2203
Training, Image segmentation, Limiting, Image recognition, Codes,
Perturbation methods, Adversarial learning,
BibRef
Liang, S.Y.[Si-Yuan],
Wu, B.Y.[Bao-Yuan],
Fan, Y.B.[Yan-Bo],
Wei, X.X.[Xing-Xing],
Cao, X.C.[Xiao-Chun],
Parallel Rectangle Flip Attack: A Query-based Black-box Attack
against Object Detection,
ICCV21(7677-7687)
IEEE DOI
2203
Costs, Perturbation methods, Detectors, Object detection,
Predictive models, Search problems, Task analysis,
Detection and localization in 2D and 3D
BibRef
Yuan, J.[Jianhe],
He, Z.H.[Zhi-Hai],
Consistency-Sensitivity Guided Ensemble Black-Box Adversarial Attacks
in Low-Dimensional Spaces,
ICCV21(7758-7766)
IEEE DOI
2203
Deep learning, Sensitivity, Design methodology,
Computational modeling, Neural networks, Task analysis,
Recognition and classification
BibRef
Lu, Y.T.[Yan-Tao],
Du, X.Y.[Xue-Ying],
Sun, B.K.[Bing-Kun],
Ren, H.N.[Hai-Ning],
Velipasalar, S.[Senem],
Fabricate-Vanish: An Effective and Transferable Black-Box Adversarial
Attack Incorporating Feature Distortion,
ICIP21(809-813)
IEEE DOI
2201
Deep learning, Adaptation models, Image processing,
Neural networks, Noise reduction, Distortion, Adversarial Examples
BibRef
Kim, B.C.[Byeong Cheon],
Yu, Y.J.[Young-Joon],
Ro, Y.M.[Yong Man],
Robust Decision-Based Black-Box Adversarial Attack via Coarse-To-Fine
Random Search,
ICIP21(3048-3052)
IEEE DOI
2201
Deep learning, Image processing, Estimation, Robustness,
Optimization, Adversarial attack, black-box attack, decision-based,
random search
BibRef
Wang, H.P.[Hui-Po],
Yu, N.[Ning],
Fritz, M.[Mario],
Hijack-GAN: Unintended-Use of Pretrained, Black-Box GANs,
CVPR21(7868-7877)
IEEE DOI
2111
Industries, Codes, Image synthesis,
Computational modeling, Process control, Aerospace electronics
BibRef
Xiao, Y.[Yanru],
Wang, C.[Cong],
You See What I Want You to See: Exploring Targeted Black-Box
Transferability Attack for Hash-based Image Retrieval Systems,
CVPR21(1934-1943)
IEEE DOI
2111
Codes, Image retrieval, Multimedia databases,
Pattern recognition, Classification algorithms, Image storage
BibRef
Li, X.D.[Xiao-Dan],
Li, J.F.[Jin-Feng],
Chen, Y.F.[Yue-Feng],
Ye, S.[Shaokai],
He, Y.[Yuan],
Wang, S.H.[Shu-Hui],
Su, H.[Hang],
Xue, H.[Hui],
QAIR: Practical Query-efficient Black-Box Attacks for Image Retrieval,
CVPR21(3329-3338)
IEEE DOI
2111
Visualization, Databases, Image retrieval, Training data,
Search engines, Loss measurement, Robustness
BibRef
Dong, Y.P.[Yin-Peng],
Yang, X.[Xiao],
Deng, Z.J.[Zhi-Jie],
Pang, T.Y.[Tian-Yu],
Xiao, Z.[Zihao],
Su, H.[Hang],
Zhu, J.[Jun],
Black-box Detection of Backdoor Attacks with Limited Information and
Data,
ICCV21(16462-16471)
IEEE DOI
2203
Training, Deep learning, Neural networks, Training data,
Predictive models, Prediction algorithms, Adversarial learning,
BibRef
Byun, J.[Junyoung],
Go, H.[Hyojun],
Kim, C.[Changick],
On the Effectiveness of Small Input Noise for Defending Against
Query-based Black-Box Attacks,
WACV22(3819-3828)
IEEE DOI
2202
Deep learning, Codes, Additives,
Computational modeling, Neural networks, Estimation,
Adversarial Attack and Defense Methods Deep Learning
BibRef
Feng, X.J.[Xin-Jie],
Yao, H.X.[Hong-Xun],
Che, W.B.[Wen-Bin],
Zhang, S.P.[Sheng-Ping],
An Effective Way to Boost Black-box Adversarial Attack,
MMMod20(I:393-404).
Springer DOI
2003
BibRef
Yang, C.L.[Cheng-Lin],
Kortylewski, A.[Adam],
Xie, C.[Cihang],
Cao, Y.Z.[Yin-Zhi],
Yuille, A.L.[Alan L.],
Patchattack: A Black-box Texture-based Attack with Reinforcement
Learning,
ECCV20(XXVI:681-698).
Springer DOI
2011
BibRef
Andriushchenko, M.[Maksym],
Croce, F.[Francesco],
Flammarion, N.[Nicolas],
Hein, M.[Matthias],
Square Attack: A Query-efficient Black-box Adversarial Attack via
Random Search,
ECCV20(XXIII:484-501).
Springer DOI
2011
BibRef
Li, J.,
Ji, R.,
Liu, H.,
Liu, J.,
Zhong, B.,
Deng, C.,
Tian, Q.,
Projection Probability-Driven Black-Box Attack,
CVPR20(359-368)
IEEE DOI
2008
Perturbation methods, Sensors, Optimization, Sparse matrices,
Compressed sensing, Google, Neural networks
BibRef
Li, H.,
Xu, X.,
Zhang, X.,
Yang, S.,
Li, B.,
QEBA: Query-Efficient Boundary-Based Blackbox Attack,
CVPR20(1218-1227)
IEEE DOI
2008
Perturbation methods, Estimation, Predictive models,
Machine learning, Cats, Pipelines, Neural networks
BibRef
Rahmati, A.,
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Frossard, P.[Pascal],
Dai, H.,
GeoDA: A Geometric Framework for Black-Box Adversarial Attacks,
CVPR20(8443-8452)
IEEE DOI
2008
Perturbation methods, Estimation, Covariance matrices,
Gaussian distribution, Measurement, Neural networks, Robustness
BibRef
Brunner, T.,
Diehl, F.,
Le, M.T.,
Knoll, A.,
Guessing Smart:
Biased Sampling for Efficient Black-Box Adversarial Attacks,
ICCV19(4957-4965)
IEEE DOI
2004
application program interfaces, cloud computing,
feature extraction, image classification, security of data, Training
BibRef
Liu, Y.J.[Yu-Jia],
Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
Frossard, P.[Pascal],
A Geometry-Inspired Decision-Based Attack,
ICCV19(4889-4897)
IEEE DOI
2004
Deal with adversarial attack.
geometry, image classification, image recognition, neural nets,
security of data, black-box settings, Gaussian noise
BibRef
Huang, Q.,
Katsman, I.,
Gu, Z.,
He, H.,
Belongie, S.,
Lim, S.,
Enhancing Adversarial Example Transferability With an Intermediate
Level Attack,
ICCV19(4732-4741)
IEEE DOI
2004
cryptography, neural nets, optimisation, black-box transferability,
source model, target models, adversarial examples,
Artificial intelligence
BibRef
Shi, Y.C.[Yu-Cheng],
Wang, S.[Siyu],
Han, Y.H.[Ya-Hong],
Curls and Whey: Boosting Black-Box Adversarial Attacks,
CVPR19(6512-6520).
IEEE DOI
2002
BibRef
Wang, S.,
Shi, Y.,
Han, Y.,
Universal Perturbation Generation for Black-box Attack Using
Evolutionary Algorithms,
ICPR18(1277-1282)
IEEE DOI
1812
Perturbation methods, Evolutionary computation, Sociology,
Statistics, Training, Neural networks, Robustness
BibRef
Narodytska, N.,
Kasiviswanathan, S.,
Simple Black-Box Adversarial Attacks on Deep Neural Networks,
PRIV17(1310-1318)
IEEE DOI
1709
Knowledge engineering, Network architecture,
Neural networks, Robustness, Training
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
VAE, Variational Autoencoder .