Assistive Signals for Deep Neural Network Classifiers
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Robyn A. Owens | Ajmal Mian | Camilo Pestana | Wei Liu | David G. Glance | R. Owens | A. Mian | Wei Liu | D. Glance | Camilo Pestana
[1] Zhitao Gong,et al. Strike (With) a Pose: Neural Networks Are Easily Fooled by Strange Poses of Familiar Objects , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Keith Manville,et al. APRICOT: A Dataset of Physical Adversarial Attacks on Object Detection , 2019, ECCV.
[3] Xin Liu,et al. DPATCH: An Adversarial Patch Attack on Object Detectors , 2018, SafeAI@AAAI.
[4] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[5] Hassan Foroosh,et al. CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild , 2018, ICLR.
[6] Jason Yosinski,et al. Deep neural networks are easily fooled: High confidence predictions for unrecognizable images , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Wei Liu,et al. Adversarial Perturbations Prevail in the Y-Channel of the YCbCr Color Space , 2020, ArXiv.
[8] Tom Goldstein,et al. Certified Defenses for Adversarial Patches , 2020, ICLR.
[9] Luca Rigazio,et al. Towards Deep Neural Network Architectures Robust to Adversarial Examples , 2014, ICLR.
[10] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Chenxi Liu,et al. Adversarial Attacks Beyond the Image Space , 2017, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[12] Chun-Liang Li,et al. Beyond Pixel Norm-Balls: Parametric Adversaries using an Analytically Differentiable Renderer , 2018, ICLR.
[13] Wei Liu,et al. Defense-friendly Images in Adversarial Attacks: Dataset and Metrics for Perturbation Difficulty , 2020, ArXiv.
[14] Aleksander Madry,et al. Towards Deep Learning Models Resistant to Adversarial Attacks , 2017, ICLR.
[15] Logan Engstrom,et al. Synthesizing Robust Adversarial Examples , 2017, ICML.
[16] Siwei Lyu,et al. Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches , 2019, BMVC.
[17] Leonidas J. Guibas,et al. ShapeNet: An Information-Rich 3D Model Repository , 2015, ArXiv.
[18] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[19] Ajmal Mian,et al. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey , 2018, IEEE Access.
[20] Samy Bengio,et al. Adversarial examples in the physical world , 2016, ICLR.
[21] Sergey Ioffe,et al. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.
[22] Jonathon Shlens,et al. Explaining and Harnessing Adversarial Examples , 2014, ICLR.
[23] Jascha Sohl-Dickstein,et al. Adversarial Reprogramming of Neural Networks , 2018, ICLR.
[24] Joan Bruna,et al. Intriguing properties of neural networks , 2013, ICLR.