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Springer DOI
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IEEE DOI
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Perturbation methods, Training, Neural networks, Image denoising,
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IET-IPR(16), No. 2, 2022, pp. 378-392.
DOI Link
2201
BibRef
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Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack,
MultMed(24), 2022, pp. 3807-3822.
IEEE DOI
2208
Noise reduction, Kernel, Task analysis, Image denoising,
Image quality, Noise measurement, Deep learning, adversarial attack
BibRef
Yang, D.[Dong],
Chen, W.[Wei],
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DTFA: Adversarial attack with discrete cosine transform noise and
target features on deep neural networks,
IET-IPR(17), No. 5, 2023, pp. 1464-1477.
DOI Link
2304
adversarial example, image classification, regional sampling, target attack
BibRef
Ying, C.Y.[Cheng-Yang],
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Consistent attack: Universal adversarial perturbation on embodied
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PRL(168), 2023, pp. 57-63.
Elsevier DOI
2304
Embodied agent, Vision navigation, Deep neural networks,
Universal adversarial noise
BibRef
Li, Y.Z.[Yue-Zun],
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Image classification, Adversarial examples, Adversarial robustness
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PR(138), 2023, pp. 109387.
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2303
Robust learning, Training with noisy labels,
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Robust object recognition, Attention-based dropout,
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Evaluating generative networks using Gaussian mixtures of image
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WACV23(279-288)
IEEE DOI
2302
Image resolution, Inverse problems, Computational modeling,
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Deep Image Destruction: Vulnerability of Deep Image-to-Image Models
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ICPR22(1287-1293)
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2212
Degradation, Training, Analytical models, Perturbation methods,
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Thakur, N.[Nupur],
Li, B.X.[Bao-Xin],
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos,
ArtOfRobust22(130-137)
IEEE DOI
2210
Training, Deep learning, Perturbation methods, Surveillance,
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Removing Adversarial Noise in Class Activation Feature Space,
ICCV21(7858-7867)
IEEE DOI
2203
Training, Deep learning, Adaptation models, Perturbation methods,
Computational modeling, Noise reduction, Adversarial learning,
Transfer/Low-shot/Semi/Unsupervised Learning
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Zhang, C.[Cheng],
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Countering Adversarial Examples:
Combining Input Transformation and Noisy Training,
AROW21(102-111)
IEEE DOI
2112
Training, Image coding, Quantization (signal),
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2201
Deep learning, Visualization, Perturbation methods, Gaussian noise,
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Adaptive Noise Injection for Training Stochastic Student Networks
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ICPR21(7587-7594)
IEEE DOI
2105
Training, Adaptation models, Adaptive systems,
Computational modeling, Stochastic processes, Machine learning,
stochastic networks
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Yan, B.,
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Cooling-Shrinking Attack:
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CVPR20(987-996)
IEEE DOI
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Target tracking, Generators, Heating systems, Perturbation methods,
Object tracking, Training
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Yi, C.,
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Standards, Gaussian noise, Tensors, Deep Learning,
Data Augmentation
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Liu, X.,
Xiao, T.,
Si, S.,
Cao, Q.,
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CVPR20(279-287)
IEEE DOI
2008
Neural networks, Robustness, Stochastic processes, Training,
Random variables, Gaussian noise, Mathematical model
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Dong, X.,
Han, J.,
Chen, D.,
Liu, J.,
Bian, H.,
Ma, Z.,
Li, H.,
Wang, X.,
Zhang, W.,
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Robust Superpixel-Guided Attentional Adversarial Attack,
CVPR20(12892-12901)
IEEE DOI
2008
Perturbation methods, Robustness, Noise measurement,
Image color analysis, Pipelines, Agriculture
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Borkar, T.,
Heide, F.,
Karam, L.J.,
Defending Against Universal Attacks Through Selective Feature
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CVPR20(706-716)
IEEE DOI
2008
Perturbation methods, Training, Robustness, Noise reduction,
Image restoration, Transforms
BibRef
Li, G.,
Ding, S.,
Luo, J.,
Liu, C.,
Enhancing Intrinsic Adversarial Robustness via Feature Pyramid
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CVPR20(797-805)
IEEE DOI
2008
Noise reduction, Robustness, Training, Image restoration,
Noise measurement, Decoding, Neural networks
BibRef
Shi, Y.,
Han, Y.,
Tian, Q.,
Polishing Decision-Based Adversarial Noise With a Customized Sampling,
CVPR20(1027-1035)
IEEE DOI
2008
Gaussian distribution, Sensitivity, Noise reduction, Optimization,
Image coding, Robustness, Standards
BibRef
He, Z.[Zhezhi],
Rakin, A.S.[Adnan Siraj],
Fan, D.L.[De-Liang],
Parametric Noise Injection: Trainable Randomness to Improve Deep Neural
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CVPR19(588-597).
IEEE DOI
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Kaneko, T.[Takuhiro],
Harada, T.[Tatsuya],
Blur, Noise, and Compression Robust Generative Adversarial Networks,
CVPR21(13574-13584)
IEEE DOI
2111
Degradation, Training, Adaptation models, Image coding, Uncertainty,
Computational modeling
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Kaneko, T.[Takuhiro],
Harada, T.[Tatsuya],
Noise Robust Generative Adversarial Networks,
CVPR20(8401-8411)
IEEE DOI
2008
Training, Noise measurement, Generators,
Noise robustness, Gaussian noise, Image generation
BibRef
Kaneko, T.[Takuhiro],
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Harada, T.[Tatsuya],
Label-Noise Robust Generative Adversarial Networks,
CVPR19(2462-2471).
IEEE DOI
2002
BibRef
Xie, C.[Cihang],
Wu, Y.X.[Yu-Xin],
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Feature Denoising for Improving Adversarial Robustness,
CVPR19(501-509).
IEEE DOI
2002
BibRef
Prakash, A.,
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Deflecting Adversarial Attacks with Pixel Deflection,
CVPR18(8571-8580)
IEEE DOI
1812
Perturbation methods, Transforms, Minimization, Robustness,
Noise reduction, Training
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Liao, F.,
Liang, M.,
Dong, Y.,
Pang, T.,
Hu, X.,
Zhu, J.,
Defense Against Adversarial Attacks Using High-Level Representation
Guided Denoiser,
CVPR18(1778-1787)
IEEE DOI
1812
Training, Perturbation methods, Noise reduction,
Image reconstruction, Predictive models, Neural networks, Adaptation models
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Adversarial Attacks .