On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples

27 February 2018

Papers citing "On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples"

33 / 33 papers shown

Title
DCT-Shield: A Robust Frequency Domain Defense against Malicious Image Editing Aniruddha Bala Rohit Chowdhury Rohan Jaiswal Siddharth Roheda DiffM AAML 73 0 0 24 Apr 2025
AdvAD: Exploring Non-Parametric Diffusion for Imperceptible Adversarial Attacks Jin Li Ziqiang He Anwei Luo Jian-Fang Hu Zhong Wang Xiangui Kang DiffM 61 0 0 12 Mar 2025
PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual Strategies for Imperceptible Adversarial Attacks Jin Li Zitong Yu Ziqiang He Zhong Wang Xiangui Kang AAML 77 0 0 15 Dec 2024
The Adaptive Arms Race: Redefining Robustness in AI Security Ilias Tsingenopoulos Vera Rimmer Davy Preuveneers Fabio Pierazzi Lorenzo Cavallaro Wouter Joosen AAML 70 0 0 20 Dec 2023
Vulnerability Analysis of Transformer-based Optical Character Recognition to Adversarial Attacks Lucas Beerens D. Higham 33 1 0 28 Nov 2023
Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang Michael Backes Xiao Zhang AAML 40 1 0 06 Oct 2023
Towards Generic and Controllable Attacks Against Object Detection Guopeng Li Yue Xu Jian Ding Guisong Xia AAML 34 6 0 23 Jul 2023
ExploreADV: Towards exploratory attack for Neural Networks Tianzuo Luo Yuyi Zhong S. Khoo AAML 22 1 0 01 Jan 2023
Towards Good Practices in Evaluating Transfer Adversarial Attacks Zhengyu Zhao Hanwei Zhang Renjue Li R. Sicre Laurent Amsaleg Michael Backes AAML 21 20 0 17 Nov 2022
Adversarial Robustness for Tabular Data through Cost and Utility Awareness Klim Kireev B. Kulynych Carmela Troncoso AAML 26 16 0 27 Aug 2022
Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognition Guangke Chen Zhe Zhao Fu Song Sen Chen Lingling Fan Feng Wang Jiashui Wang AAML 20 36 0 07 Jun 2022
Saliency Attack: Towards Imperceptible Black-box Adversarial Attack Zeyu Dai Shengcai Liu Ke Tang Qing Li AAML 24 11 0 04 Jun 2022
Adversarial Training for High-Stakes Reliability Daniel M. Ziegler Seraphina Nix Lawrence Chan Tim Bauman Peter Schmidt-Nielsen ... Noa Nabeshima Benjamin Weinstein-Raun D. Haas Buck Shlegeris Nate Thomas AAML 30 59 0 03 May 2022
Frequency-driven Imperceptible Adversarial Attack on Semantic Similarity Cheng Luo Qinliang Lin Weicheng Xie Bizhu Wu Jinheng Xie Linlin Shen AAML 28 100 0 10 Mar 2022
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks Weiran Lin Keane Lucas Lujo Bauer Michael K. Reiter Mahmood Sharif AAML 29 5 0 28 Dec 2021
Human Imperceptible Attacks and Applications to Improve Fairness Xinru Hua Huanzhong Xu Jose H. Blanchet V. Nguyen AAML 19 3 0 30 Nov 2021
Pareto Adversarial Robustness: Balancing Spatial Robustness and Sensitivity-based Robustness Ke Sun Mingjie Li Zhouchen Lin AAML 19 2 0 03 Nov 2021
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning Neil G. Marchant Benjamin I. P. Rubinstein Scott Alfeld MU AAML 20 69 0 17 Sep 2021
Imperceptible Adversarial Examples by Spatial Chroma-Shift A. Aydin Deniz Sen Berat Tuna Karli Oguz Hanoglu A. Temi̇zel AAML 18 16 0 05 Aug 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
Algorithmic Bias and Data Bias: Understanding the Relation between Distributionally Robust Optimization and Data Curation Agnieszka Słowik Léon Bottou FaML 40 19 0 17 Jun 2021
Localized Uncertainty Attacks Ousmane Amadou Dia Theofanis Karaletsos C. Hazirbas Cristian Canton Ferrer I. Kabul E. Meijer AAML 21 2 0 17 Jun 2021
We Can Always Catch You: Detecting Adversarial Patched Objects WITH or WITHOUT Signature Binxiu Liang Jiachun Li Jianjun Huang AAML 19 12 0 09 Jun 2021
Achieving Adversarial Robustness Requires An Active Teacher Chao Ma Lexing Ying 19 1 0 14 Dec 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 29 48 0 19 Oct 2020
Optimizing Information Loss Towards Robust Neural Networks Philip Sperl Konstantin Böttinger AAML 13 3 0 07 Aug 2020
Advanced Evasion Attacks and Mitigations on Practical ML-Based Phishing Website Classifiers Yusi Lei Sen Chen Lingling Fan Fu Song Yang Liu AAML 28 50 0 15 Apr 2020
Quantum noise protects quantum classifiers against adversaries Yuxuan Du Min-hsiu Hsieh Tongliang Liu Dacheng Tao Nana Liu AAML 22 110 0 20 Mar 2020
Imperceptible Adversarial Attacks on Tabular Data Vincent Ballet X. Renard Jonathan Aigrain Thibault Laugel P. Frossard Marcin Detyniecki 8 72 0 08 Nov 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
Quantifying Perceptual Distortion of Adversarial Examples Matt Jordan N. Manoj Surbhi Goel A. Dimakis 11 38 0 21 Feb 2019
Random Spiking and Systematic Evaluation of Defenses Against Adversarial Examples Huangyi Ge Sze Yiu Chau Bruno Ribeiro Ninghui Li AAML 21 1 0 05 Dec 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 261 3,109 0 04 Nov 2016

On the Suitability of LpL_pLp​-norms for Creating and Preventing Adversarial Examples

Papers citing "On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples"

On the Suitability of $L_p$ -norms for Creating and Preventing Adversarial Examples