Exploring the Space of Adversarial Images

19 October 2015

Papers citing "Exploring the Space of Adversarial Images"

30 / 30 papers shown

Title
On the Relationship Between Interpretability and Explainability in Machine Learning Benjamin Leblanc Pascal Germain FaML 33 0 0 20 Nov 2023
Learning video embedding space with Natural Language Supervision P. Uppala Abhishek Bamotra S. Priya Vaidehi Joshi CLIP 29 1 0 25 Mar 2023
Identifying Adversarially Attackable and Robust Samples Vyas Raina Mark Gales AAML 38 3 0 30 Jan 2023
Guidance Through Surrogate: Towards a Generic Diagnostic Attack Muzammal Naseer Salman Khan Fatih Porikli Fahad Shahbaz Khan AAML 28 1 0 30 Dec 2022
Efficiently Finding Adversarial Examples with DNN Preprocessing Avriti Chauhan Mohammad Afzal Hrishikesh Karmarkar Y. Elboher Kumar Madhukar Guy Katz AAML 32 0 0 16 Nov 2022
Multi-concept adversarial attacks Vibha Belavadi Yan Zhou Murat Kantarcioglu B. Thuraisingham AAML 35 0 0 19 Oct 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 38 236 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 35 2 0 25 Feb 2021
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
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 29 73 0 07 Aug 2020
Understanding Adversarial Examples from the Mutual Influence of Images and Perturbations Chaoning Zhang Philipp Benz Tooba Imtiaz In-So Kweon SSL AAML 22 118 0 13 Jul 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
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 72 63 0 02 Mar 2020
Analysis of Random Perturbations for Robust Convolutional Neural Networks Adam Dziedzic S. Krishnan OOD AAML 24 1 0 08 Feb 2020
Mixup Inference: Better Exploiting Mixup to Defend Adversarial Attacks Tianyu Pang Kun Xu Jun Zhu AAML 28 103 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 37 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 30 2 0 04 Dec 2018
Learning to Defend by Learning to Attack Haoming Jiang Zhehui Chen Yuyang Shi Bo Dai T. Zhao 21 22 0 03 Nov 2018
Characterizing Adversarial Examples Based on Spatial Consistency Information for Semantic Segmentation Chaowei Xiao Ruizhi Deng Bo-wen Li Feng Yu M. Liu D. Song AAML 19 99 0 11 Oct 2018
Improving the Generalization of Adversarial Training with Domain Adaptation Chuanbiao Song Kun He Liwei Wang J. Hopcroft AAML OOD 28 131 0 01 Oct 2018
Cautious Deep Learning Yotam Hechtlinger Barnabás Póczós Larry A. Wasserman 35 63 0 24 May 2018
Retrieval-Augmented Convolutional Neural Networks for Improved Robustness against Adversarial Examples Jake Zhao Kyunghyun Cho AAML 24 20 0 26 Feb 2018
Adversarial Example Defenses: Ensembles of Weak Defenses are not Strong Warren He James Wei Xinyun Chen Nicholas Carlini D. Song AAML 43 242 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 29 118 0 26 May 2017
On the Limitation of Convolutional Neural Networks in Recognizing Negative Images Hossein Hosseini Baicen Xiao Mayoore S. Jaiswal Radha Poovendran 19 121 0 20 Mar 2017
Universal adversarial perturbations Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi Omar Fawzi P. Frossard AAML 50 2,514 0 26 Oct 2016
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 16 367 0 31 Aug 2016
A study of the effect of JPG compression on adversarial images Gintare Karolina Dziugaite Zoubin Ghahramani Daniel M. Roy AAML 38 531 0 02 Aug 2016
Measuring Neural Net Robustness with Constraints Osbert Bastani Yani Andrew Ioannou Leonidas Lampropoulos Dimitrios Vytiniotis A. Nori A. Criminisi AAML 17 422 0 24 May 2016
Adversarial Manipulation of Deep Representations S. Sabour Yanshuai Cao Fartash Faghri David J. Fleet GAN AAML 33 286 0 16 Nov 2015