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IEEE DOI
2003
Training data, Neural networks, Reverse engineering,
Machine learning, Robustness, Training data, Feature extraction,
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Elsevier DOI
2006
Adversarial attacks, Adversarial examples, Deep learning, Perturbation analysis
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IEEE DOI
2007
Robustness, Perturbation methods, Training, Deep learning,
Computer architecture, Neural networks, Signal to noise ratio,
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Ji, J.T.[Jun-Tao],
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Resisting Large Data Variations via Introspective Transformation
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WACV20(3069-3078)
IEEE DOI
2006
Training, Testing, Robustness, Training data,
Linear programming, Resists
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Kim, D.[Donghyun],
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Multi-way Encoding for Robustness,
WACV20(1341-1349)
IEEE DOI
2006
To counter adversarial attacks.
Encoding, Robustness, Perturbation methods, Training,
Biological system modeling, Neurons, Correlation
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Folz, J.,
Palacio, S.,
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Dengel, A.,
Adversarial Defense based on Structure-to-Signal Autoencoders,
WACV20(3568-3577)
IEEE DOI
2006
Perturbation methods, Semantics, Robustness, Predictive models,
Training, Decoding, Neural networks
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Peterson, J.[Joshua],
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Human Uncertainty Makes Classification More Robust,
ICCV19(9616-9625)
IEEE DOI
2004
CIFAR10H dataset.
To make deep network robust ot adversarial attacks.
convolutional neural nets, learning (artificial intelligence),
pattern classification, classification performance,
Dogs
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Wang, J.,
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Bilateral Adversarial Training:
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ICCV19(6628-6637)
IEEE DOI
2004
entropy, learning (artificial intelligence), neural nets,
security of data, adversarial attacks, Data models
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Ye, S.,
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Liu, S.,
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Adversarial Robustness vs. Model Compression, or Both?,
ICCV19(111-120)
IEEE DOI
2004
minimax techniques, neural nets, security of data,
adversarial attacks, concurrent adversarial training
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Moosavi-Dezfooli, S.M.[Seyed-Mohsen],
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Robustness via Curvature Regularization, and Vice Versa,
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IEEE DOI
2002
Adversarial training leads to more linear boundaries.
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Feature Denoising for Improving Adversarial Robustness,
CVPR19(501-509).
IEEE DOI
2002
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Parametric Noise Injection: Trainable Randomness to Improve Deep Neural
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CVPR19(588-597).
IEEE DOI
2002
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Label-Noise Robust Generative Adversarial Networks,
CVPR19(2462-2471).
IEEE DOI
2002
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Stutz, D.[David],
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Disentangling Adversarial Robustness and Generalization,
CVPR19(6969-6980).
IEEE DOI
2002
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Miyazato, S.,
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Reinforcing the Robustness of a Deep Neural Network to Adversarial
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ICIP19(884-888)
IEEE DOI
1910
convolutional neural network, adversarial example, color quantization
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Ramanathan, T.,
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Robustness of Saak Transform Against Adversarial Attacks,
ICIP19(2531-2535)
IEEE DOI
1910
Saak transform, Adversarial attacks, Deep Neural Networks, Image Classification
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When Causal Intervention Meets Adversarial Examples and Image Masking
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ICIP19(3811-3815)
IEEE DOI
1910
Causal Reasoning, Adversarial Example, Adversarial Robustness,
Interpretable Deep Learning, Visual Reasoning
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Storer, J.,
Deflecting Adversarial Attacks with Pixel Deflection,
CVPR18(8571-8580)
IEEE DOI
1812
Perturbation methods, Transforms, Minimization, Robustness,
Noise reduction, Training, Computer vision
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Mummadi, C.K.,
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Defending Against Universal Perturbations With Shared Adversarial
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ICCV19(4927-4936)
IEEE DOI
2004
image classification, image segmentation, neural nets,
universal perturbations, shared adversarial training,
Computational modeling
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Chen, H.,
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Chang, S.,
Pan, J.,
Chen, Y.,
Wei, W.,
Juan, D.,
Improving Adversarial Robustness via Guided Complement Entropy,
ICCV19(4880-4888)
IEEE DOI
2004
entropy, learning (artificial intelligence), neural nets,
probability, adversarial defense, adversarial robustness,
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Bai, Y.,
Feng, Y.,
Wang, Y.,
Dai, T.,
Xia, S.,
Jiang, Y.,
Hilbert-Based Generative Defense for Adversarial Examples,
ICCV19(4783-4792)
IEEE DOI
2004
feature extraction, Hilbert transforms, neural nets,
security of data, scan mode, advanced Hilbert curve scan order
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Jang, Y.,
Zhao, T.,
Hong, S.,
Lee, H.,
Adversarial Defense via Learning to Generate Diverse Attacks,
ICCV19(2740-2749)
IEEE DOI
2004
learning (artificial intelligence), neural nets,
pattern classification, security of data, adversarial defense, Machine learning
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Mustafa, A.,
Khan, S.,
Hayat, M.,
Goecke, R.,
Shen, J.,
Shao, L.,
Adversarial Defense by Restricting the Hidden Space of Deep Neural
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ICCV19(3384-3393)
IEEE DOI
2004
convolutional neural nets, feature extraction,
image classification, image representation, Iterative methods
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Defending Against Adversarial Attacks by Randomized Diversification,
CVPR19(11218-11225).
IEEE DOI
2002
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Adversarial Defense by Stratified Convolutional Sparse Coding,
CVPR19(11439-11448).
IEEE DOI
2002
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CVPR19(9221-9229).
IEEE DOI
2002
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IEEE DOI
2002
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Evading Defenses to Transferable Adversarial Examples by
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IEEE DOI
2002
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IEEE DOI
2002
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IEEE DOI
2002
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2002
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ShieldNets: Defending Against Adversarial Attacks Using Probabilistic
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2002
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Image Decomposition and Classification Through a Generative Model,
ICIP19(400-404)
IEEE DOI
1910
Generative model, classification, adversarial defense
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Ji, J.,
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Multi-Scale Defense of Adversarial Images,
ICIP19(4070-4074)
IEEE DOI
1910
deep learning, adversarial images, defense, multi-scale, image evolution
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Agarwal, C.,
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Improving Robustness to Adversarial Examples by Encouraging
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ICIP19(3801-3805)
IEEE DOI
1910
Adversarial Machine Learning, Robustness, Defenses, Deep Learning
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Bridging Machine Learning and Cryptography in Defence Against
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Naseer, M.,
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Local Gradients Smoothing: Defense Against Localized Adversarial
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WACV19(1300-1307)
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1904
data compression, feature extraction, gradient methods,
image classification, image coding, image representation,
High frequency
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Akhtar, N.,
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Defense Against Universal Adversarial Perturbations,
CVPR18(3389-3398)
IEEE DOI
1812
Perturbation methods, Training, Computational modeling, Detectors,
Neural networks, Robustness, Integrated circuits
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Liao, F.,
Liang, M.,
Dong, Y.,
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Defense Against Adversarial Attacks Using High-Level Representation
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CVPR18(1778-1787)
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1812
Training, Perturbation methods, Noise reduction,
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ARC: Adversarial Robust Cuts for Semi-Supervised and Multi-label
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WiCV18(1986-19862)
IEEE DOI
1812
Markov random fields, Task analysis, Training, Testing,
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Karim, R.,
Islam, M.A.,
Mohammed, N.,
Bruce, N.D.B.,
On the Robustness of Deep Learning Models to Universal Adversarial
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CRV18(55-62)
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Perturbation methods, Computational modeling, Neural networks,
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Semantic Segmentation
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Towards Robust Deep Neural Networks with BANG,
WACV18(803-811)
IEEE DOI
1806
image processing, learning (artificial intelligence),
neural nets, BANG technique, adversarial image utilization,
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Lu, J.,
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Forsyth, D.A.,
SafetyNet: Detecting and Rejecting Adversarial Examples Robustly,
ICCV17(446-454)
IEEE DOI
1802
image colour analysis, image reconstruction,
learning (artificial intelligence), neural nets,
BibRef
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Bayesian Learning, Bayes Network, Bayesian Networks .