EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks

21 April 2020

Papers citing "EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness against Adversarial Attacks"

41 / 41 papers shown

Title
TriQDef: Disrupting Semantic and Gradient Alignment to Prevent Adversarial Patch Transferability in Quantized Neural Networks Amira Guesmi B. Ouni Muhammad Shafique AAML MQ 60 0 0 16 Aug 2025
Breaking the Limits of Quantization-Aware Defenses: QADT-R for Robustness Against Patch-Based Adversarial Attacks in QNNs Amira Guesmi B. Ouni Muhammad Shafique MQ AAML 222 0 0 10 Mar 2025
AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses Nicholas Carlini Javier Rando Edoardo Debenedetti Milad Nasr F. Tramèr AAML ELM 159 7 0 03 Mar 2025
Exploring the Robustness and Transferability of Patch-Based Adversarial Attacks in Quantized Neural Networks Amira Guesmi B. Ouni Mohamed Bennai AAML 291 0 0 22 Nov 2024
Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers Mohammad Javad Askarizadeh Ebrahim Farahmand Jorge Castro-Godínez A. Mahani Laura Cabrera-Quiros C. Salazar-García AAML 124 0 0 17 Apr 2024
Investigating the Impact of Quantization on Adversarial Robustness Qun Li Yuan Meng Chen Tang Jiacheng Jiang Zhi Wang 142 10 0 08 Apr 2024
The Impact of Quantization on the Robustness of Transformer-based Text Classifiers Seyed Parsa Neshaei Yasaman Boreshban Gholamreza Ghassem-Sani Seyed Abolghasem Mirroshandel MQ 132 0 0 08 Mar 2024
Improving the Robustness of Quantized Deep Neural Networks to White-Box Attacks using Stochastic Quantization and Information-Theoretic Ensemble Training Saurabh Farkya Aswin Raghavan Avi Ziskind 164 0 0 30 Nov 2023
Quantization Aware Attack: Enhancing Transferable Adversarial Attacks by Model QuantizationIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2023 Yulong Yang Chenhao Lin Qian Li Subrat Kishore Dutta Haoran Fan Dawei Zhou Nannan Wang Tongliang Liu Chao Shen AAML MQ 226 20 0 10 May 2023
Improved Robustness Against Adaptive Attacks With Ensembles and Error-Correcting Output Codes Thomas Philippon Christian Gagné AAML 83 1 0 04 Mar 2023
On the Robustness of Randomized Ensembles to Adversarial PerturbationsInternational Conference on Machine Learning (ICML), 2023 Hassan Dbouk Naresh R Shanbhag AAML 204 8 0 02 Feb 2023
Ares: A System-Oriented Wargame Framework for Adversarial ML Farhan Ahmed Pratik Vaishnavi Kevin Eykholt Amir Rahmati AAML 133 8 0 24 Oct 2022
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness GamesInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022 Maria-Florina Balcan Rattana Pukdee Pradeep Ravikumar Hongyang R. Zhang AAML 148 12 0 23 Oct 2022
Providing Error Detection for Deep Learning Image Classifiers Using Self-Explainability M. M. Karimi Azin Heidarshenas W. Edmonson 158 0 0 15 Oct 2022
Adversarial Ensemble Training by Jointly Learning Label Dependencies and Member ModelsInternational Conference on Intelligent Computing (ICIC), 2022 Lele Wang B. Liu UQCV 231 6 0 29 Jun 2022
Increasing Confidence in Adversarial Robustness EvaluationsNeural Information Processing Systems (NeurIPS), 2022 Roland S. Zimmermann Wieland Brendel Florian Tramèr Nicholas Carlini AAML 131 18 0 28 Jun 2022
Adversarial Vulnerability of Randomized EnsemblesInternational Conference on Machine Learning (ICML), 2022 Hassan Dbouk Naresh R Shanbhag AAML 117 7 0 14 Jun 2022
Building Robust Ensembles via Margin BoostingInternational Conference on Machine Learning (ICML), 2022 Dinghuai Zhang Hongyang R. Zhang Aaron Courville Yoshua Bengio Pradeep Ravikumar A. Suggala AAML UQCV 112 17 0 07 Jun 2022
On the Perils of Cascading Robust ClassifiersInternational Conference on Learning Representations (ICLR), 2022 Ravi Mangal Zifan Wang Chi Zhang Klas Leino C. Păsăreanu Matt Fredrikson AAML 148 0 0 01 Jun 2022
On Adversarial Robustness of Large-scale Audio Visual LearningIEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022 Juncheng Billy Li Shuhui Qu Xinjian Li Po-Yao (Bernie) Huang Florian Metze AAML 154 9 0 23 Mar 2022
All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelines Yuxuan Zhang B. Dong Felix Heide AAML 136 10 0 16 Dec 2021
Saliency Diversified Deep Ensemble for Robustness to Adversaries Alexander A. Bogun Dimche Kostadinov Damian Borth AAML FedML 97 5 0 07 Dec 2021
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural NetworksNeural Information Processing Systems (NeurIPS), 2021 Hassan Dbouk Naresh R Shanbhag AAML 115 8 0 28 Oct 2021
Improving Adversarial Robustness for Free with Snapshot Ensemble Yihao Wang AAML UQCV 96 1 0 07 Oct 2021
Tensor Normalization and Full Distribution Training Wolfgang Fuhl OOD 150 5 0 06 Sep 2021
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Lin Wang Navid Kardan M. Shah AAML 382 284 0 01 Aug 2021
The art of defense: letting networks fool the attackerIEEE Transactions on Information Forensics and Security (IEEE TIFS), 2021 Jinlai Zhang Lyvjie Chen Binbin Liu Bojun Ouyang Jihong Zhu Minchi Kuang Houqing Wang Yanmei Meng AAML 3DPC 202 18 0 07 Apr 2021
Understanding the Error in Evaluating Adversarial Robustness Pengfei Xia Ziqiang Li Hongjing Niu Bin Li AAML ELM 118 5 0 07 Jan 2021
Voting based ensemble improves robustness of defensive models Devvrit Minhao Cheng Cho-Jui Hsieh Inderjit Dhillon OOD FedML AAML 113 12 0 28 Nov 2020
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence Chang Liao Yao Cheng Chengfang Fang Jie Shi 117 1 0 28 Sep 2020
Adversarial robustness via stochastic regularization of neural activation sensitivity Gil Fidel Ron Bitton Ziv Katzir A. Shabtai AAML 84 2 0 23 Sep 2020
Improving Resistance to Adversarial Deformations by Regularizing GradientsNeurocomputing (Neurocomputing), 2020 Pengfei Xia Bin Li AAML 110 4 0 29 Aug 2020
TREND: Transferability based Robust ENsemble Design Deepak Ravikumar Sangamesh Kodge Isha Garg Kaushik Roy OOD AAML 107 5 0 04 Aug 2020
Provably Robust Adversarial ExamplesInternational Conference on Learning Representations (ICLR), 2020 Dimitar I. Dimitrov Gagandeep Singh Timon Gehr Martin Vechev AAML 170 12 0 23 Jul 2020
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey S. Silva Peyman Najafirad AAML OOD 199 145 0 01 Jul 2020
Tricking Adversarial Attacks To Fail Blerta Lindqvist AAML 65 0 0 08 Jun 2020
Enhancing Certified Robustness via Smoothed Weighted Ensembling Chizhou Liu Yunzhen Feng Ranran Wang Bin Dong AAML 157 12 0 19 May 2020
Improved Gradient based Adversarial Attacks for Quantized NetworksAAAI Conference on Artificial Intelligence (AAAI), 2020 Kartik Gupta Thalaiyasingam Ajanthan MQ 98 21 0 30 Mar 2020
Randomization matters. How to defend against strong adversarial attacksInternational Conference on Machine Learning (ICML), 2020 Rafael Pinot Raphael Ettedgui Geovani Rizk Y. Chevaleyre Jamal Atif AAML 217 64 0 26 Feb 2020
On Adaptive Attacks to Adversarial Example DefensesNeural Information Processing Systems (NeurIPS), 2020 Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 458 894 0 19 Feb 2020
Error-Correcting Output Codes with Ensemble Diversity for Robust Learning in Neural NetworksAAAI Conference on Artificial Intelligence (AAAI), 2019 Yang Song Qiyu Kang Wee Peng Tay AAML 198 23 0 30 Nov 2019