Evasion Attacks Against Bayesian Predictive Models

• URL: http://arxiv.org/abs/2506.09640v1
• Date: Wed, 11 Jun 2025 11:53:20 GMT
• Title: Evasion Attacks Against Bayesian Predictive Models
• Authors: Pablo G. Arce, Roi Naveiro, David Ríos Insua,
• Abstract summary: This paper introduces a general methodology for designing optimal evasion attacks against such models.<n>We investigate two adversarial objectives: perturbing specific point predictions and altering the entire posterior predictive distribution.<n>For both scenarios, we propose novel gradient-based attacks and study their implementation and properties in various computational setups.
• Score: 1.8570591025615457
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There is an increasing interest in analyzing the behavior of machine learning systems against adversarial attacks. However, most of the research in adversarial machine learning has focused on studying weaknesses against evasion or poisoning attacks to predictive models in classical setups, with the susceptibility of Bayesian predictive models to attacks remaining underexplored. This paper introduces a general methodology for designing optimal evasion attacks against such models. We investigate two adversarial objectives: perturbing specific point predictions and altering the entire posterior predictive distribution. For both scenarios, we propose novel gradient-based attacks and study their implementation and properties in various computational setups.

Related papers

• Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning [54.26807397329468]
  This work explores a previously overlooked vulnerability in distributed deep learning systems.<n>An adversary who intercepts the intermediate features transmitted between them can still pose a serious threat.<n>We propose an exploitation strategy specifically designed for distributed settings.
  arXiv  Detail & Related papers  (2025-07-09T20:09:00Z)
• Indiscriminate Disruption of Conditional Inference on Multivariate Gaussians [60.22542847840578]
  Despite advances in adversarial machine learning, inference for Gaussian models in the presence of an adversary is notably understudied. We consider a self-interested attacker who wishes to disrupt a decisionmaker's conditional inference and subsequent actions by corrupting a set of evidentiary variables. To avoid detection, the attacker also desires the attack to appear plausible wherein plausibility is determined by the density of the corrupted evidence.
  arXiv  Detail & Related papers  (2024-11-21T17:46:55Z)
• Adversarial Attacks Against Uncertainty Quantification [10.655660123083607]
  This work focuses on a different adversarial scenario in which the attacker is still interested in manipulating the uncertainty estimate. In particular, the goal is to undermine the use of machine-learning models when their outputs are consumed by a downstream module or by a human operator.
  arXiv  Detail & Related papers  (2023-09-19T12:54:09Z)
• Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving [18.72382517467458]
  We propose a novel adversarial backdoor attack against trajectory prediction models. Our attack affects the victim at training time via naturalistic, hence stealthy, poisoned samples crafted using a novel two-step approach. We show that the proposed attack is highly effective, as it can significantly hinder the performance of prediction models.
  arXiv  Detail & Related papers  (2023-06-27T19:15:06Z)
• Targeted Attacks on Timeseries Forecasting [0.6719751155411076]
  We propose a novel formulation of Directional, Amplitudinal, and Temporal targeted adversarial attacks on time series forecasting models. These targeted attacks create a specific impact on the amplitude and direction of the output prediction. Our experimental results show how targeted attacks on time series models are viable and are more powerful in terms of statistical similarity.
  arXiv  Detail & Related papers  (2023-01-27T06:09:42Z)
• On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach [42.81632484264218]
  In machine learning models, perturbations of fractions of the training set (poisoning) can seriously undermine the model accuracy. This paper aims to implement a novel hash-based ensemble approach that protects random forest against untargeted, random poisoning attacks.
  arXiv  Detail & Related papers  (2022-09-28T11:41:38Z)
• AdvDO: Realistic Adversarial Attacks for Trajectory Prediction [87.96767885419423]
  Trajectory prediction is essential for autonomous vehicles to plan correct and safe driving behaviors. We devise an optimization-based adversarial attack framework to generate realistic adversarial trajectories. Our attack can lead an AV to drive off road or collide into other vehicles in simulation.
  arXiv  Detail & Related papers  (2022-09-19T03:34:59Z)
• Robust Multivariate Time-Series Forecasting: Adversarial Attacks and Defense Mechanisms [17.75675910162935]
  A new attack pattern negatively impacts the forecasting of a target time series. We develop two defense strategies to mitigate the impact of such attack. Experiments on real-world datasets confirm that our attack schemes are powerful.
  arXiv  Detail & Related papers  (2022-07-19T22:00:41Z)
• Adversarial Robustness of Deep Reinforcement Learning based Dynamic Recommender Systems [50.758281304737444]
  We propose to explore adversarial examples and attack detection on reinforcement learning-based interactive recommendation systems. We first craft different types of adversarial examples by adding perturbations to the input and intervening on the casual factors. Then, we augment recommendation systems by detecting potential attacks with a deep learning-based classifier based on the crafted data.
  arXiv  Detail & Related papers  (2021-12-02T04:12:24Z)
• Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples [107.38834819682315]
  We study a conceptually simple approach to defend few-shot classifiers against adversarial attacks. We propose a simple attack-agnostic detection method, using the concept of self-similarity and filtering. Our evaluation on the miniImagenet (MI) and CUB datasets exhibit good attack detection performance.
  arXiv  Detail & Related papers  (2021-10-24T05:46:03Z)
• Adversarial attacks against Bayesian forecasting dynamic models [1.8275108630751844]
  AML studies how to manipulate data to fool inference engines. In this paper, we propose a decision analysis based attacking strategy against Bayesian forecasting dynamic models.
  arXiv  Detail & Related papers  (2021-10-20T21:23:45Z)
• Adversarial Attack and Defense of Structured Prediction Models [58.49290114755019]
  In this paper, we investigate attacks and defenses for structured prediction tasks in NLP. The structured output of structured prediction models is sensitive to small perturbations in the input. We propose a novel and unified framework that learns to attack a structured prediction model using a sequence-to-sequence model.
  arXiv  Detail & Related papers  (2020-10-04T15:54:03Z)

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.