Optimizing Information Loss Towards Robust Neural Networks
Authors: Philip Sperl, Konstantin Böttinger | Published: 2020-08-07 | Updated: 2020-09-29
敵対的サンプル
Robust Deep Reinforcement Learning through Adversarial Loss
Authors: Tuomas Oikarinen, Wang Zhang, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng | Published: 2020-08-05 | Updated: 2021-11-10
TREND: Transferability based Robust ENsemble Design
Authors: Deepak Ravikumar, Sangamesh Kodge, Isha Garg, Kaushik Roy | Published: 2020-08-04 | Updated: 2021-03-30
TEAM: We Need More Powerful Adversarial Examples for DNNs
Authors: Yaguan Qian, Ximin Zhang, Bin Wang, Wei Li, Zhaoquan Gu, Haijiang Wang, Wassim Swaileh | Published: 2020-07-31 | Updated: 2020-08-10
Reachable Sets of Classifiers and Regression Models: (Non-)Robustness Analysis and Robust Training
Authors: Anna-Kathrin Kopetzki, Stephan Günnemann | Published: 2020-07-28 | Updated: 2021-05-12
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning
Authors: Jirong Yi, Raghu Mudumbai, Weiyu Xu | Published: 2020-07-28
Provably Robust Adversarial Examples
Authors: Dimitar I. Dimitrov, Gagandeep Singh, Timon Gehr, Martin Vechev | Published: 2020-07-23 | Updated: 2022-03-17
Security and Machine Learning in the Real World
Authors: Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, Jerry Li | Published: 2020-07-13
ManiGen: A Manifold Aided Black-box Generator of Adversarial Examples
Authors: Guanxiong Liu, Issa Khalil, Abdallah Khreishah, Abdulelah Algosaibi, Adel Aldalbahi, Mohammed Alaneem, Abdulaziz Alhumam, Mohammed Anan | Published: 2020-07-11
Generating Adversarial Inputs Using A Black-box Differential Technique
Authors: João Batista Pereira Matos Juúnior, Lucas Carvalho Cordeiro, Marcelo d'Amorim, Xiaowei Huang | Published: 2020-07-10