Provably Minimally-Distorted Adversarial Examples

29 September 2017

Papers citing "Provably Minimally-Distorted Adversarial Examples"

22 / 22 papers shown

Title
Fast Certification of Vision-Language Models Using Incremental Randomized Smoothing Ashutosh Nirala Ameya Joshi Chinmay Hegde S Sarkar VLM 36 0 0 15 Nov 2023
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness Ameya Joshi Minh Pham Minsu Cho Leonid Boytsov Filipe Condessa J. Zico Kolter C. Hegde UQCV AAML 32 2 0 12 May 2022
RADA: Robust Adversarial Data Augmentation for Camera Localization in Challenging Weather Jialu Wang Muhamad Risqi U. Saputra C. Lu Niki Trigon Andrew Markham 30 2 0 05 Dec 2021
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification S. Silva Arun Das I. Scarff Peyman Najafirad AAML 20 1 0 05 Apr 2021
ROBY: Evaluating the Robustness of a Deep Model by its Decision Boundaries Jinyin Chen Zhen Wang Haibin Zheng Jun Xiao Zhaoyan Ming AAML 21 5 0 18 Dec 2020
Adversarial Machine Learning in Image Classification: A Survey Towards the Defender's Perspective G. R. Machado Eugênio Silva R. Goldschmidt AAML 33 157 0 08 Sep 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 43 371 0 30 Apr 2020
What Deep CNNs Benefit from Global Covariance Pooling: An Optimization Perspective Qilong Wang Li Zhang Banggu Wu Dongwei Ren P. Li W. Zuo Q. Hu 22 21 0 25 Mar 2020
ART: Abstraction Refinement-Guided Training for Provably Correct Neural Networks Xuankang Lin He Zhu R. Samanta Suresh Jagannathan AAML 27 28 0 17 Jul 2019
Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward A. Qayyum Muhammad Usama Junaid Qadir Ala I. Al-Fuqaha AAML 27 187 0 29 May 2019
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints Xing Hu Ling Liang Lei Deng Shuangchen Li Xinfeng Xie Yu Ji Yufei Ding Chang Liu T. Sherwood Yuan Xie AAML MLAU 23 36 0 10 Mar 2019
Verification of Recurrent Neural Networks Through Rule Extraction Qinglong Wang Kaixuan Zhang Xue Liu C. Lee Giles AAML 28 18 0 14 Nov 2018
Evading classifiers in discrete domains with provable optimality guarantees B. Kulynych Jamie Hayes N. Samarin Carmela Troncoso AAML 21 19 0 25 Oct 2018
Training for Faster Adversarial Robustness Verification via Inducing ReLU Stability Kai Y. Xiao Vincent Tjeng Nur Muhammad (Mahi) Shafiullah A. Madry AAML OOD 12 199 0 09 Sep 2018
Distributionally Adversarial Attack T. Zheng Changyou Chen K. Ren OOD 21 121 0 16 Aug 2018
Formal Security Analysis of Neural Networks using Symbolic Intervals Shiqi Wang Kexin Pei Justin Whitehouse Junfeng Yang Suman Jana AAML 34 473 0 28 Apr 2018
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm Wenjie Ruan Min Wu Youcheng Sun Xiaowei Huang Daniel Kroening Marta Kwiatkowska AAML 15 38 0 16 Apr 2018
Evaluating Robustness of Neural Networks with Mixed Integer Programming Vincent Tjeng Kai Y. Xiao Russ Tedrake AAML 52 117 0 20 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks A. Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 86 11,872 0 19 Jun 2017
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 249 1,842 0 03 Feb 2017
Safety Verification of Deep Neural Networks Xiaowei Huang Marta Kwiatkowska Sen Wang Min Wu AAML 183 933 0 21 Oct 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 326 5,847 0 08 Jul 2016