Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

1 April 2018

Battista Biggio

Papers citing "Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning"

35 / 135 papers shown

Title
Data Poisoning Attacks on Regression Learning and Corresponding Defenses Nicolas Müller Daniel Kowatsch Konstantin Böttinger AAML 10 18 0 15 Sep 2020
Defending Regression Learners Against Poisoning Attacks Sandamal Weerasinghe S. Erfani T. Alpcan C. Leckie Justin Kopacz AAML 16 0 0 21 Aug 2020
Dynamic Defense Against Byzantine Poisoning Attacks in Federated Learning Nuria Rodríguez-Barroso Eugenio Martínez-Cámara M. V. Luzón Francisco Herrera FedML AAML 24 35 0 29 Jul 2020
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review Yansong Gao Bao Gia Doan Zhi-Li Zhang Siqi Ma Jiliang Zhang Anmin Fu Surya Nepal Hyoungshick Kim AAML 36 221 0 21 Jul 2020
Data Poisoning Attacks Against Federated Learning Systems Vale Tolpegin Stacey Truex Mehmet Emre Gursoy Ling Liu FedML 31 640 0 16 Jul 2020
Subpopulation Data Poisoning Attacks Matthew Jagielski Giorgio Severi Niklas Pousette Harger Alina Oprea AAML SILM 24 114 0 24 Jun 2020
Sponge Examples: Energy-Latency Attacks on Neural Networks Ilia Shumailov Yiren Zhao Daniel Bates Nicolas Papernot Robert D. Mullins Ross J. Anderson SILM 19 127 0 05 Jun 2020
Blind Backdoors in Deep Learning Models Eugene Bagdasaryan Vitaly Shmatikov AAML FedML SILM 46 298 0 08 May 2020
Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness Pu Zhao Pin-Yu Chen Payel Das Karthikeyan N. Ramamurthy Xue Lin AAML 64 185 0 30 Apr 2020
PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks Junfeng Guo Zelun Kong Cong Liu AAML 27 1 0 24 Mar 2020
Dynamic Backdoor Attacks Against Machine Learning Models A. Salem Rui Wen Michael Backes Shiqing Ma Yang Zhang AAML 48 271 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
Entangled Watermarks as a Defense against Model Extraction Hengrui Jia Christopher A. Choquette-Choo Varun Chandrasekaran Nicolas Papernot WaLM AAML 13 218 0 27 Feb 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
NNoculation: Catching BadNets in the Wild A. Veldanda Kang Liu Benjamin Tan Prashanth Krishnamurthy Farshad Khorrami Ramesh Karri Brendan Dolan-Gavitt S. Garg AAML OnRL 13 20 0 19 Feb 2020
Adversarial Machine Learning -- Industry Perspectives Ramnath Kumar Magnus Nyström J. Lambert Andrew Marshall Mario Goertzel Andi Comissoneru Matt Swann Sharon Xia AAML SILM 29 232 0 04 Feb 2020
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer Hong Chang Virat Shejwalkar Reza Shokri Amir Houmansadr FedML 26 167 0 24 Dec 2019
White-Box Target Attack for EEG-Based BCI Regression Problems Lubin Meng Chin-Teng Lin T. Jung Dongrui Wu AAML 31 42 0 07 Nov 2019
Data Poisoning Attacks to Local Differential Privacy Protocols Xiaoyu Cao Jinyuan Jia Neil Zhenqiang Gong AAML 33 76 0 05 Nov 2019
Differentiable Convex Optimization Layers Akshay Agrawal Brandon Amos Shane T. Barratt Stephen P. Boyd Steven Diamond Zico Kolter 45 640 0 28 Oct 2019
Deep Neural Rejection against Adversarial Examples Angelo Sotgiu Ambra Demontis Marco Melis Battista Biggio Giorgio Fumera Xiaoyi Feng Fabio Roli AAML 22 68 0 01 Oct 2019
Big Data Analytics for Large Scale Wireless Networks: Challenges and Opportunities Hongning Dai Raymond Chi-Wing Wong Hao Wang Zibin Zheng A. Vasilakos AI4CE GNN 22 65 0 02 Sep 2019
Helen: Maliciously Secure Coopetitive Learning for Linear Models Wenting Zheng Raluca A. Popa Joseph E. Gonzalez Ion Stoica FedML 27 144 0 16 Jul 2019
Poisoning Attacks with Generative Adversarial Nets Luis Muñoz-González Bjarne Pfitzner Matteo Russo Javier Carnerero-Cano Emil C. Lupu AAML 21 63 0 18 Jun 2019
Privacy Risks of Securing Machine Learning Models against Adversarial Examples Liwei Song Reza Shokri Prateek Mittal SILM MIACV AAML 6 235 0 24 May 2019
Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder Ji Feng Qi-Zhi Cai Zhi-Hua Zhou AAML 19 104 0 22 May 2019
Attacking Graph-based Classification via Manipulating the Graph Structure Binghui Wang Neil Zhenqiang Gong AAML 39 155 0 01 Mar 2019
Contamination Attacks and Mitigation in Multi-Party Machine Learning Jamie Hayes O. Ohrimenko AAML FedML 25 74 0 08 Jan 2019
Stealing Neural Networks via Timing Side Channels Vasisht Duddu D. Samanta D. V. Rao V. Balas AAML MLAU FedML 33 133 0 31 Dec 2018
Analyzing Federated Learning through an Adversarial Lens A. Bhagoji Supriyo Chakraborty Prateek Mittal S. Calo FedML 191 1,034 0 29 Nov 2018
Stronger Data Poisoning Attacks Break Data Sanitization Defenses Pang Wei Koh Jacob Steinhardt Percy Liang 6 240 0 02 Nov 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
Data Poisoning Attacks in Contextual Bandits Yuzhe Ma Kwang-Sung Jun Lihong Li Xiaojin Zhu AAML 17 67 0 17 Aug 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 59 928 0 04 Jun 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 40 1,391 0 08 Dec 2017