Exploring the Space of Adversarial Images

19 October 2015

Papers citing "Exploring the Space of Adversarial Images"

20 / 20 papers shown

Title
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 26 0 0 20 Nov 2023
Learning video embedding space with Natural Language Supervision P. Uppala Abhishek Bamotra S. Priya Vaidehi Joshi CLIP 15 1 0 25 Mar 2023
Identifying Adversarially Attackable and Robust Samples Vyas Raina Mark J. F. Gales AAML 25 3 0 30 Jan 2023
Efficiently Finding Adversarial Examples with DNN Preprocessing Avriti Chauhan Mohammad Afzal Hrishikesh Karmarkar Y. Elboher Kumar Madhukar Guy Katz AAML 24 0 0 16 Nov 2022
Multi-concept adversarial attacks Vibha Belavadi Yan Zhou Murat Kantarcioglu B. Thuraisingham AAML 30 0 0 19 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Saeed Mian Navid Kardan M. Shah AAML 26 235 0 01 Aug 2021
Understanding Robustness in Teacher-Student Setting: A New Perspective Zhuolin Yang Zhaoxi Chen Tiffany Cai Xinyun Chen Bo-wen Li Yuandong Tian AAML 27 2 0 25 Feb 2021
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 25 73 0 07 Aug 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 29 371 0 30 Apr 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 50 63 0 02 Mar 2020
Analysis of Random Perturbations for Robust Convolutional Neural Networks Adam Dziedzic S. Krishnan OOD AAML 16 1 0 08 Feb 2020
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang Kun Xu Jun Zhu AAML 20 103 0 25 Sep 2019
Taking Care of The Discretization Problem: A Comprehensive Study of the Discretization Problem and A Black-Box Adversarial Attack in Discrete Integer Domain Lei Bu Yuchao Duan Fu Song Zhe Zhao AAML 20 18 0 19 May 2019
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic Computation S. Garg Zahra Ghodsi Carmit Hazay Yuval Ishai Antonio Marcedone Muthuramakrishnan Venkitasubramaniam FedML 20 2 0 04 Dec 2018
Learning to Defend by Learning to Attack Haoming Jiang Zhehui Chen Yuyang Shi Bo Dai T. Zhao 8 22 0 03 Nov 2018
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation Chaowei Xiao Ruizhi Deng Bo-wen Li F. I. F. Richard Yu M. Liu D. Song AAML 16 99 0 11 Oct 2018
Cautious Deep Learning Yotam Hechtlinger Barnabás Póczós Larry A. Wasserman 24 62 0 24 May 2018
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini D. Song AAML 27 242 0 15 Jun 2017
Robustness of classifiers to universal perturbations: a geometric perspective Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard Stefano Soatto AAML 24 118 0 26 May 2017
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 24 2,509 0 26 Oct 2016