Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks

19 March 2018

Papers citing "Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks"

33 / 133 papers shown

Title
Bullseye Polytope: A Scalable Clean-Label Poisoning Attack with Improved Transferability H. Aghakhani Dongyu Meng Yu-Xiang Wang Christopher Kruegel Giovanni Vigna AAML 31 105 0 01 May 2020
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware Deqiang Li Qianmu Li Yanfang Ye Shouhuai Xu AAML 17 13 0 15 Apr 2020
A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks Samuel Deng Sanjam Garg S. Jha Saeed Mahloujifar Mohammad Mahmoody Abhradeep Thakurta 20 3 0 26 Mar 2020
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks Junfeng Guo Zelun Kong Cong Liu AAML 24 1 0 24 Mar 2020
Dynamic Backdoor Attacks Against Machine Learning Models A. Salem Rui Wen Michael Backes Shiqing Ma Yang Zhang AAML 43 271 0 07 Mar 2020
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers Giorgio Severi J. Meyer Scott E. Coull Alina Oprea AAML SILM 29 18 0 02 Mar 2020
On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping Sanghyun Hong Varun Chandrasekaran Yigitcan Kaya Tudor Dumitras Nicolas Papernot AAML 28 136 0 26 Feb 2020
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models Shawn Shan Emily Wenger Jiayun Zhang Huiying Li Haitao Zheng Ben Y. Zhao PICV MU 29 24 0 19 Feb 2020
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks Moshe Kravchik A. Shabtai AAML 14 1 0 07 Feb 2020
Local Model Poisoning Attacks to Byzantine-Robust Federated Learning Minghong Fang Xiaoyu Cao Jinyuan Jia Neil Zhenqiang Gong AAML OOD FedML 48 1,075 0 26 Nov 2019
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey Olakunle Ibitoye Rana Abou-Khamis Mohamed el Shehaby Ashraf Matrawy M. O. Shafiq AAML 37 68 0 06 Nov 2019
Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version] Jacopo Cortellazzi Feargus Pendlebury Daniel Arp Erwin Quiring Fabio Pierazzi Lorenzo Cavallaro AAML 35 0 0 05 Nov 2019
A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models Ren Pang Hua Shen Xinyang Zhang S. Ji Yevgeniy Vorobeychik Xiaopu Luo Alex Liu Ting Wang AAML 13 2 0 05 Nov 2019
Orchestrating the Development Lifecycle of Machine Learning-Based IoT Applications: A Taxonomy and Survey Bin Qian Jie Su Z. Wen D. N. Jha Yinhao Li ... Albert Y. Zomaya Omer F. Rana Lizhe Wang Maciej Koutny R. Ranjan 28 4 0 11 Oct 2019
Hidden Trigger Backdoor Attacks Aniruddha Saha Akshayvarun Subramanya Hamed Pirsiavash 13 613 0 30 Sep 2019
FENCE: Feasible Evasion Attacks on Neural Networks in Constrained Environments Alesia Chernikova Alina Oprea AAML 19 35 0 23 Sep 2019
Adversarial Vulnerability Bounds for Gaussian Process Classification M. Smith Kathrin Grosse Michael Backes Mauricio A. Alvarez AAML 8 9 0 19 Sep 2019
Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges Jinyuan Jia Neil Zhenqiang Gong AAML SILM 15 16 0 17 Sep 2019
Explaining Vulnerabilities to Adversarial Machine Learning through Visual Analytics Yuxin Ma Tiankai Xie Jundong Li Ross Maciejewski AAML 13 67 0 17 Jul 2019
Adversarial Security Attacks and Perturbations on Machine Learning and Deep Learning Methods Arif Siddiqi AAML 19 11 0 17 Jul 2019
Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs Soheil Kolouri Aniruddha Saha Hamed Pirsiavash Heiko Hoffmann AAML 27 231 0 26 Jun 2019
Terminal Brain Damage: Exposing the Graceless Degradation in Deep Neural Networks Under Hardware Fault Attacks Sanghyun Hong Pietro Frigo Yigitcan Kaya Cristiano Giuffrida Tudor Dumitras AAML 22 211 0 03 Jun 2019
An Investigation of Data Poisoning Defenses for Online Learning Yizhen Wang Somesh Jha Kamalika Chaudhuri AAML 13 5 0 28 May 2019
Transferable Clean-Label Poisoning Attacks on Deep Neural Nets Chen Zhu Yifan Jiang Ali Shafahi Hengduo Li Gavin Taylor Christoph Studer Tom Goldstein 27 283 0 15 May 2019
Better the Devil you Know: An Analysis of Evasion Attacks using Out-of-Distribution Adversarial Examples Vikash Sehwag A. Bhagoji Liwei Song Chawin Sitawarin Daniel Cullina M. Chiang Prateek Mittal OODD 29 26 0 05 May 2019
Gotta Catch Ém All: Using Honeypots to Catch Adversarial Attacks on Neural Networks Shawn Shan Emily Wenger Bolun Wang Yangqiu Song Haitao Zheng Ben Y. Zhao 23 71 0 18 Apr 2019
Attacking Graph-based Classification via Manipulating the Graph Structure Binghui Wang Neil Zhenqiang Gong AAML 39 154 0 01 Mar 2019
Stronger Data Poisoning Attacks Break Data Sanitization Defenses Pang Wei Koh Jacob Steinhardt Percy Liang 6 240 0 02 Nov 2018
Security Analysis of Deep Neural Networks Operating in the Presence of Cache Side-Channel Attacks Sanghyun Hong Michael Davinroy Yigitcan Kaya S. Locke Ian Rackow Kevin Kulda Dana Dachman-Soled Tudor Dumitras MIACV 25 90 0 08 Oct 2018
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks Ambra Demontis Marco Melis Maura Pintor Matthew Jagielski Battista Biggio Alina Oprea Cristina Nita-Rotaru Fabio Roli SILM AAML 19 11 0 08 Sep 2018
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models A. Salem Yang Zhang Mathias Humbert Pascal Berrang Mario Fritz Michael Backes MIACV MIALM 39 928 0 04 Jun 2018
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks Kang Liu Brendan Dolan-Gavitt S. Garg AAML 24 1,021 0 30 May 2018
Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks Ali Shafahi Yifan Jiang Mahyar Najibi Octavian Suciu Christoph Studer Tudor Dumitras Tom Goldstein AAML 22 1,076 0 03 Apr 2018