Detecting and Mitigating Adversarial Perturbations for Robust Face Recognition
- Gaurav Goswami
- Akshay Agarwal
- et al.
- 2019
- IJCV
Paper
Publications
970 results for Trustworthy AI
On the utility of conditional generation based mutual information for characterizing adversarial subspaces
- Chia-Yi Hsu
- Pei Hsuan Lu
- et al.
- 2018
- GlobalSIP 2018
Conference paperIs ordered weighted ℓ1 regularized regression robust to adversarial perturbation? a case study on Oscar
- Pin-Yu Chen
- Bhanukiran Vinzamuri
- et al.
- 2018
- GlobalSIP 2018
Conference paperAutoZOOM: Autoencoder-based zeroth order optimization method for attacking black-box neural networks
- Chun-Chen Tu
- Paishun Ting
- et al.
- 2019
- AAAI 2019
Conference paperResisting adversarial attacks using Gaussian mixture variational autoencoders
- Partha Ghosh
- Arpan Losalka
- et al.
- 2019
- AAAI 2019
Conference paperCNN-Cert: An efficient framework for certifying robustness of convolutional neural networks
- Akhilan Boopathy
- Tsui Wei Weng
- et al.
- 2019
- AAAI 2019
Conference paperADMM attack: An enhanced adversarial attack for deep neural networks with undetectable distortions
- Pu Zhao
- Kaidi Xu
- et al.
- 2019
- ASP-DAC 2019
Conference paperAn ADMM-based universal framework for adversarial attacks on deep neural networks
- Pu Zhao
- Yanzhi Wang
- et al.
- 2018
- MM 2018
Conference paperWhy the Failure? How Adversarial Examples Can Provide Insights for Interpretable Machine Learning
- Richard Tomsett
- Amy Widdicombe
- et al.
- 2018
- FUSION 2018
Conference paperOn the Limitation of MagNet Defense Against L1-Based Adversarial Examples
- Pei Hsuan Lu
- Pin-Yu Chen
- et al.
- 2018
- DSN-W 2018
Conference paper