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"

50 / 133 papers shown

Title
Federated Learning for Cyber Physical Systems: A Comprehensive Survey Minh K. Quan P. Pathirana M. Wijayasundara S. Setunge Dinh C. Nguyen Christopher G. Brinton David J. Love H. Vincent Poor AI4CE 61 0 0 08 May 2025
Traceback of Poisoning Attacks to Retrieval-Augmented Generation Baolei Zhang Haoran Xin Minghong Fang Zhuqing Liu Biao Yi Tong Li Zheli Liu SILM AAML 80 0 0 30 Apr 2025
Technical Insights and Legal Considerations for Advancing Federated Learning in Bioinformatics Daniele Malpetti Marco Scutari Francesco Gualdi Jessica van Setten Sander van der Laan Saskia Haitjema Aaron Mark Lee Isabelle Hering Francesca Mangili FedML AI4CE 109 1 0 12 Mar 2025
Decoding the Black Box: Integrating Moral Imagination with Technical AI Governance Krti Tallam 49 1 0 09 Mar 2025
FedSV: Byzantine-Robust Federated Learning via Shapley Value Khaoula Otmani Rachid Elazouzi Vincent Labatut FedML AAML 90 2 0 24 Feb 2025
Poisoning Prevention in Federated Learning and Differential Privacy via Stateful Proofs of Execution Norrathep Rattanavipanon Ivan de Oliviera Nunes 86 0 0 28 Jan 2025
Timber! Poisoning Decision Trees Stefano Calzavara Lorenzo Cazzaro Massimo Vettori AAML 35 0 0 01 Oct 2024
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation Jiahao Xu Zikai Zhang Rui Hu 49 6 0 02 Sep 2024
A Survey on Machine Unlearning: Techniques and New Emerged Privacy Risks Hengzhu Liu Ping Xiong Tianqing Zhu Philip S. Yu 47 6 0 10 Jun 2024
Nonlinear Transformations Against Unlearnable Datasets T. Hapuarachchi Jing Lin Kaiqi Xiong Mohamed Rahouti Gitte Ost 32 1 0 05 Jun 2024
Learning from Uncertain Data: From Possible Worlds to Possible Models Jiongli Zhu Su Feng Boris Glavic Babak Salimi 39 0 0 28 May 2024
Toward a Quantum Information System Cybersecurity Taxonomy and Testbed: Exploiting a Unique Opportunity for Early Impact Benjamin Blakely Joaquin Chung Alec Poczatek Ryan Syed Raj Kettimuthu 18 1 0 18 Apr 2024
Robust Decision Aggregation with Adversarial Experts Yongkang Guo Yuqing Kong 37 0 0 13 Mar 2024
Manipulating hidden-Markov-model inferences by corrupting batch data William N. Caballero Jose Manuel Camacho Tahir Ekin Roi Naveiro AAML 23 1 0 19 Feb 2024
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 31 16 0 02 Feb 2024
Logit Poisoning Attack in Distillation-based Federated Learning and its Countermeasures Yonghao Yu Shunan Zhu Jinglu Hu AAML FedML 37 0 0 31 Jan 2024
Poisoned ChatGPT Finds Work for Idle Hands: Exploring Developers' Coding Practices with Insecure Suggestions from Poisoned AI Models Sanghak Oh Kiho Lee Seonhye Park Doowon Kim Hyoungshick Kim SILM 29 16 0 11 Dec 2023
Beyond Detection: Unveiling Fairness Vulnerabilities in Abusive Language Models Yueqing Liang Lu Cheng Ali Payani Kai Shu 28 3 0 15 Nov 2023
On the Proactive Generation of Unsafe Images From Text-To-Image Models Using Benign Prompts Yixin Wu Ning Yu Michael Backes Yun Shen Yang Zhang DiffM 59 8 0 25 Oct 2023
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning Byung-Kwan Lee Junho Kim Yonghyun Ro AAML 33 9 0 14 Jul 2023
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection Edoardo Gabrielli Dimitri Belli Vittorio Miori Gabriele Tolomei AAML 13 4 0 29 Mar 2023
Cybersecurity of AI medical devices: risks, legislation, and challenges E. Biasin Erik Kamenjašević K. Ludvigsen 18 6 0 06 Mar 2023
Testing the Channels of Convolutional Neural Networks Kang Choi Donghyun Son Younghoon Kim Jiwon Seo 30 1 0 06 Mar 2023
CADeSH: Collaborative Anomaly Detection for Smart Homes Yair Meidan D. Avraham H. Libhaber A. Shabtai 21 8 0 02 Mar 2023
Poisoning Web-Scale Training Datasets is Practical Nicholas Carlini Matthew Jagielski Christopher A. Choquette-Choo Daniel Paleka Will Pearce Hyrum S. Anderson Andreas Terzis Kurt Thomas Florian Tramèr SILM 31 182 0 20 Feb 2023
Towards Unbounded Machine Unlearning M. Kurmanji Peter Triantafillou Jamie Hayes Eleni Triantafillou MU 28 124 0 20 Feb 2023
An Incremental Gray-box Physical Adversarial Attack on Neural Network Training Rabiah Al-qudah Moayad Aloqaily B. Ouni Mohsen Guizani T. Lestable AAML 38 4 0 20 Feb 2023
Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions Marwan Omar SILM AAML 33 20 0 14 Feb 2023
Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics Yahao Ding Zhaohui Yang Viet Quoc Pham Zhaoyang Zhang M. Shikh-Bahaei 36 32 0 03 Jan 2023
XMAM:X-raying Models with A Matrix to Reveal Backdoor Attacks for Federated Learning Jianyi Zhang Fangjiao Zhang Qichao Jin Zhiqiang Wang Xiaodong Lin X. Hei AAML FedML 38 1 0 28 Dec 2022
FairRoad: Achieving Fairness for Recommender Systems with Optimized Antidote Data Minghong Fang Jia-Wei Liu Michinari Momma Yi Sun 38 4 0 13 Dec 2022
Selective Amnesia: On Efficient, High-Fidelity and Blind Suppression of Backdoor Effects in Trojaned Machine Learning Models Rui Zhu Di Tang Siyuan Tang Xiaofeng Wang Haixu Tang AAML FedML 37 13 0 09 Dec 2022
Adversarial Attacks are a Surprisingly Strong Baseline for Poisoning Few-Shot Meta-Learners E. T. Oldewage J. Bronskill Richard Turner 27 3 0 23 Nov 2022
Analysis and Detectability of Offline Data Poisoning Attacks on Linear Dynamical Systems Alessio Russo AAML 6 3 0 16 Nov 2022
Data Poisoning Attacks Against Multimodal Encoders Ziqing Yang Xinlei He Zheng Li Michael Backes Mathias Humbert Pascal Berrang Yang Zhang AAML 118 46 0 30 Sep 2022
Federated Learning based on Defending Against Data Poisoning Attacks in IoT Jiayin Li Wenzhong Guo Xingshuo Han Jianping Cai Ximeng Liu AAML 83 1 0 14 Sep 2022
SNAP: Efficient Extraction of Private Properties with Poisoning Harsh Chaudhari John Abascal Alina Oprea Matthew Jagielski Florian Tramèr Jonathan R. Ullman MIACV 39 30 0 25 Aug 2022
Training-Time Attacks against k-Nearest Neighbors Ara Vartanian Will Rosenbaum Scott Alfeld 14 1 0 15 Aug 2022
Careful What You Wish For: on the Extraction of Adversarially Trained Models Kacem Khaled Gabriela Nicolescu F. Magalhães MIACV AAML 35 4 0 21 Jul 2022
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications Ali Raza Shujun Li K. Tran L. Koehl Kim Duc Tran AAML 38 3 0 18 Jul 2022
Enhanced Security and Privacy via Fragmented Federated Learning N. Jebreel J. Domingo-Ferrer Alberto Blanco-Justicia David Sánchez FedML 39 26 0 13 Jul 2022
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms Ehsan Hallaji R. Razavi-Far M. Saif AAML FedML 27 13 0 05 Jul 2022
Defending against the Label-flipping Attack in Federated Learning N. Jebreel J. Domingo-Ferrer David Sánchez Alberto Blanco-Justicia AAML 18 36 0 05 Jul 2022
FL-Defender: Combating Targeted Attacks in Federated Learning N. Jebreel J. Domingo-Ferrer AAML FedML 43 56 0 02 Jul 2022
Threat Assessment in Machine Learning based Systems L. Tidjon Foutse Khomh 27 17 0 30 Jun 2022
Edge Security: Challenges and Issues Xin Jin Charalampos Katsis Fan Sang Jiahao Sun A. Kundu Ramana Rao Kompella 49 8 0 14 Jun 2022
Certifying Data-Bias Robustness in Linear Regression Anna P. Meyer Aws Albarghouthi Loris Dántoni 35 3 0 07 Jun 2022
Backdooring Explainable Machine Learning Maximilian Noppel Lukas Peter Christian Wressnegger AAML 18 5 0 20 Apr 2022
Adversarial Analysis of the Differentially-Private Federated Learning in Cyber-Physical Critical Infrastructures Md Tamjid Hossain S. Badsha Hung M. La Haoting Shen Shafkat Islam Ibrahim Khalil X. Yi AAML 29 3 0 06 Apr 2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 51 109 0 31 Mar 2022