Subpopulation Data Poisoning Attacks

• URL: http://arxiv.org/abs/2006.14026v3
• Date: Wed, 12 May 2021 17:53:39 GMT
• Title: Subpopulation Data Poisoning Attacks
• Authors: Matthew Jagielski, Giorgio Severi, Niklas Pousette Harger, Alina Oprea
• Abstract summary: Poisoning attacks against machine learning induce adversarial modification of data used by a machine learning algorithm to selectively change its output when it is deployed. We introduce a novel data poisoning attack called a emphsubpopulation attack, which is particularly relevant when datasets are large and diverse. We design a modular framework for subpopulation attacks, instantiate it with different building blocks, and show that the attacks are effective for a variety of datasets and machine learning models.
• Score: 18.830579299974072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning systems are deployed in critical settings, but they might fail in unexpected ways, impacting the accuracy of their predictions. Poisoning attacks against machine learning induce adversarial modification of data used by a machine learning algorithm to selectively change its output when it is deployed. In this work, we introduce a novel data poisoning attack called a \emph{subpopulation attack}, which is particularly relevant when datasets are large and diverse. We design a modular framework for subpopulation attacks, instantiate it with different building blocks, and show that the attacks are effective for a variety of datasets and machine learning models. We further optimize the attacks in continuous domains using influence functions and gradient optimization methods. Compared to existing backdoor poisoning attacks, subpopulation attacks have the advantage of inducing misclassification in naturally distributed data points at inference time, making the attacks extremely stealthy. We also show that our attack strategy can be used to improve upon existing targeted attacks. We prove that, under some assumptions, subpopulation attacks are impossible to defend against, and empirically demonstrate the limitations of existing defenses against our attacks, highlighting the difficulty of protecting machine learning against this threat.

Related papers

• FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning [98.43475653490219]
  Federated learning (FL) is susceptible to poisoning attacks. FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain. We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
  arXiv  Detail & Related papers  (2023-12-07T16:56:24Z)
• Adversarial Attacks Neutralization via Data Set Randomization [3.655021726150369]
  Adversarial attacks on deep learning models pose a serious threat to their reliability and security. We propose a new defense mechanism that is rooted on hyperspace projection. We show that our solution increases the robustness of deep learning models against adversarial attacks.
  arXiv  Detail & Related papers  (2023-06-21T10:17:55Z)
• Avoid Adversarial Adaption in Federated Learning by Multi-Metric Investigations [55.2480439325792]
  Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources. FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks. We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously. MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
  arXiv  Detail & Related papers  (2023-06-06T11:44:42Z)
• Can Adversarial Examples Be Parsed to Reveal Victim Model Information? [62.814751479749695]
  In this work, we ask whether it is possible to infer data-agnostic victim model (VM) information from data-specific adversarial instances. We collect a dataset of adversarial attacks across 7 attack types generated from 135 victim models. We show that a simple, supervised model parsing network (MPN) is able to infer VM attributes from unseen adversarial attacks.
  arXiv  Detail & Related papers  (2023-03-13T21:21:49Z)
• The Space of Adversarial Strategies [6.295859509997257]
  Adversarial examples, inputs designed to induce worst-case behavior in machine learning models, have been extensively studied over the past decade. We propose a systematic approach to characterize worst-case (i.e., optimal) adversaries.
  arXiv  Detail & Related papers  (2022-09-09T20:53:11Z)
• Membership-Doctor: Comprehensive Assessment of Membership Inference Against Machine Learning Models [11.842337448801066]
  We present a large-scale measurement of different membership inference attacks and defenses. We find that some assumptions of the threat model, such as same-architecture and same-distribution between shadow and target models, are unnecessary. We are also the first to execute attacks on the real-world data collected from the Internet, instead of laboratory datasets.
  arXiv  Detail & Related papers  (2022-08-22T17:00:53Z)
• Projective Ranking-based GNN Evasion Attacks [52.85890533994233]
  Graph neural networks (GNNs) offer promising learning methods for graph-related tasks. GNNs are at risk of adversarial attacks.
  arXiv  Detail & Related papers  (2022-02-25T21:52:09Z)
• Fixed Points in Cyber Space: Rethinking Optimal Evasion Attacks in the Age of AI-NIDS [70.60975663021952]
  We study blackbox adversarial attacks on network classifiers. We argue that attacker-defender fixed points are themselves general-sum games with complex phase transitions. We show that a continual learning approach is required to study attacker-defender dynamics.
  arXiv  Detail & Related papers  (2021-11-23T23:42:16Z)
• Influence Based Defense Against Data Poisoning Attacks in Online Learning [9.414651358362391]
  Data poisoning is an attack where an attacker manipulates a fraction of data to degrade the performance of machine learning model. We propose a defense mechanism to minimize the degradation caused by the poisoned training data on a learner's model in an online setup.
  arXiv  Detail & Related papers  (2021-04-24T08:39:13Z)
• Adversarial Attack Attribution: Discovering Attributable Signals in Adversarial ML Attacks [0.7883722807601676]
  Even production systems, such as self-driving cars and ML-as-a-service offerings, are susceptible to adversarial inputs. Can perturbed inputs be attributed to the methods used to generate the attack? We introduce the concept of adversarial attack attribution and create a simple supervised learning experimental framework to examine the feasibility of discovering attributable signals in adversarial attacks.
  arXiv  Detail & Related papers  (2021-01-08T08:16:41Z)
• Learning to Attack: Towards Textual Adversarial Attacking in Real-world Situations [81.82518920087175]
  Adversarial attacking aims to fool deep neural networks with adversarial examples. We propose a reinforcement learning based attack model, which can learn from attack history and launch attacks more efficiently.
  arXiv  Detail & Related papers  (2020-09-19T09:12:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.