A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples

27 August 2016

Papers citing "A Boundary Tilting Persepective on the Phenomenon of Adversarial Examples"

43 / 43 papers shown

Title
Exploiting the Layered Intrinsic Dimensionality of Deep Models for Practical Adversarial Training Enes Altinisik Safa Messaoud H. Sencar Hassan Sajjad Sanjay Chawla AAML 48 0 0 27 May 2024
Training Image Derivatives: Increased Accuracy and Universal Robustness V. Avrutskiy 46 0 0 21 Oct 2023
Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks Ruixiang Tang Gord Lueck Rodolfo Quispe Huseyin A. Inan Janardhan Kulkarni Xia Hu 26 6 0 20 Oct 2023
Improve Video Representation with Temporal Adversarial Augmentation Jinhao Duan Quanfu Fan Hao-Ran Cheng Xiaoshuang Shi Kaidi Xu AAML AI4TS ViT 25 2 0 28 Apr 2023
Identifying Adversarially Attackable and Robust Samples Vyas Raina Mark J. F. Gales AAML 25 3 0 30 Jan 2023
REAP: A Large-Scale Realistic Adversarial Patch Benchmark Nabeel Hingun Chawin Sitawarin Jerry Li David A. Wagner AAML 31 14 0 12 Dec 2022
Textual Manifold-based Defense Against Natural Language Adversarial Examples D. M. Nguyen Anh Tuan Luu AAML 19 17 0 05 Nov 2022
A Manifold View of Adversarial Risk Wen-jun Zhang Yikai Zhang Xiaoling Hu Mayank Goswami Chao Chen Dimitris N. Metaxas AAML 19 6 0 24 Mar 2022
Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data Yaoqing Yang Ryan Theisen Liam Hodgkinson Joseph E. Gonzalez Kannan Ramchandran Charles H. Martin Michael W. Mahoney 86 17 0 06 Feb 2022
Image classifiers can not be made robust to small perturbations Zheng Dai David K Gifford VLM AAML 16 1 0 07 Dec 2021
Sparse Adversarial Video Attacks with Spatial Transformations Ronghui Mu Wenjie Ruan Leandro Soriano Marcolino Q. Ni AAML 25 18 0 10 Nov 2021
A novel network training approach for open set image recognition Md Tahmid Hossaina S. Teng Guojun Lu Ferdous Sohel 21 0 0 27 Sep 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
Scale-invariant scale-channel networks: Deep networks that generalise to previously unseen scales Ylva Jansson T. Lindeberg 11 23 0 11 Jun 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 21 57 0 25 Feb 2021
Visually Imperceptible Adversarial Patch Attacks on Digital Images Yaguan Qian Jiamin Wang Bin Wang Xiang Ling Zhaoquan Gu Chunming Wu Wassim Swaileh AAML 22 2 0 02 Dec 2020
Characterizing and Taming Model Instability Across Edge Devices Eyal Cidon Evgenya Pergament Zain Asgar Asaf Cidon Sachin Katti 14 7 0 18 Oct 2020
A Hamiltonian Monte Carlo Method for Probabilistic Adversarial Attack and Learning Hongjun Wang Guanbin Li Xiaobai Liu Liang Lin GAN AAML 16 22 0 15 Oct 2020
The Intriguing Relation Between Counterfactual Explanations and Adversarial Examples Timo Freiesleben GAN 33 62 0 11 Sep 2020
Adversarial Examples on Object Recognition: A Comprehensive Survey A. Serban E. Poll Joost Visser AAML 25 73 0 07 Aug 2020
Boundary thickness and robustness in learning models Yaoqing Yang Rekha Khanna Yaodong Yu A. Gholami Kurt Keutzer Joseph E. Gonzalez K. Ramchandran Michael W. Mahoney OOD 10 37 0 09 Jul 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 27 147 0 20 May 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 58 63 0 02 Mar 2020
Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring Sibylle Hess W. Duivesteijn D. Mocanu 20 12 0 07 Jan 2020
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Rémi Bernhard Pierre-Alain Moëllic J. Dutertre AAML MQ 24 18 0 27 Sep 2019
Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training Haichao Zhang Jianyu Wang AAML 23 230 0 24 Jul 2019
PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving Zelun Kong Junfeng Guo Ang Li Cong Liu AAML 36 126 0 09 Jul 2019
ML-LOO: Detecting Adversarial Examples with Feature Attribution Puyudi Yang Jianbo Chen Cho-Jui Hsieh Jane-ling Wang Michael I. Jordan AAML 22 101 0 08 Jun 2019
Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade T. Lindeberg 27 19 0 29 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 16 5 0 19 May 2019
A Kernelized Manifold Mapping to Diminish the Effect of Adversarial Perturbations Saeid Asgari Taghanaki Kumar Abhishek Shekoofeh Azizi Ghassan Hamarneh AAML 31 40 0 03 Mar 2019
An Information-Theoretic Explanation for the Adversarial Fragility of AI Classifiers Hui Xie Jirong Yi Weiyu Xu R. Mudumbai AAML 21 3 0 27 Jan 2019
Defending Against Universal Perturbations With Shared Adversarial Training Chaithanya Kumar Mummadi Thomas Brox J. H. Metzen AAML 18 60 0 10 Dec 2018
Robustness via curvature regularization, and vice versa Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi J. Uesato P. Frossard AAML 21 318 0 23 Nov 2018
With Friends Like These, Who Needs Adversaries? Saumya Jetley Nicholas A. Lord Philip H. S. Torr AAML 15 70 0 11 Jul 2018
Understanding Measures of Uncertainty for Adversarial Example Detection Lewis Smith Y. Gal UQCV 16 358 0 22 Mar 2018
Adversarial Defense based on Structure-to-Signal Autoencoders Joachim Folz Sebastián M. Palacio Jörn Hees Damian Borth Andreas Dengel AAML 26 31 0 21 Mar 2018
Adversarial vulnerability for any classifier Alhussein Fawzi Hamza Fawzi Omar Fawzi AAML 22 248 0 23 Feb 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Bo-wen Li Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck D. Song Michael E. Houle James Bailey AAML 26 726 0 08 Jan 2018
High Dimensional Spaces, Deep Learning and Adversarial Examples S. Dube 20 29 0 02 Jan 2018
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid Marco Melis Ambra Demontis Battista Biggio Gavin Brown Giorgio Fumera Fabio Roli AAML 19 98 0 23 Aug 2017
Analyzing the Robustness of Nearest Neighbors to Adversarial Examples Yizhen Wang S. Jha Kamalika Chaudhuri AAML 11 154 0 13 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