First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data

• URL: http://arxiv.org/abs/2109.14857v1
• Date: Thu, 30 Sep 2021 05:30:12 GMT
• Title: First to Possess His Statistics: Data-Free Model Extraction Attack on Tabular Data
• Authors: Masataka Tasumi, Kazuki Iwahana, Naoto Yanai, Katsunari Shishido, Toshiya Shimizu, Yuji Higuchi, Ikuya Morikawa, Jun Yajima
• Abstract summary: This paper presents a novel model extraction attack, named TEMPEST, under a practical data-free setting. Experiments show that our attack can achieve the same level of performance as the previous attacks. We discuss a possibility whereby TEMPEST is executed in the real world through an experiment with a medical diagnosis.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model extraction attacks are a kind of attacks where an adversary obtains a machine learning model whose performance is comparable with one of the victim model through queries and their results. This paper presents a novel model extraction attack, named TEMPEST, applicable on tabular data under a practical data-free setting. Whereas model extraction is more challenging on tabular data due to normalization, TEMPEST no longer needs initial samples that previous attacks require; instead, it makes use of publicly available statistics to generate query samples. Experiments show that our attack can achieve the same level of performance as the previous attacks. Moreover, we identify that the use of mean and variance as statistics for query generation and the use of the same normalization process as the victim model can improve the performance of our attack. We also discuss a possibility whereby TEMPEST is executed in the real world through an experiment with a medical diagnosis dataset. We plan to release the source code for reproducibility and a reference to subsequent works.

Related papers

• On the Adversarial Risk of Test Time Adaptation: An Investigation into Realistic Test-Time Data Poisoning [49.17494657762375]
  Test-time adaptation (TTA) updates the model weights during the inference stage using testing data to enhance generalization. Existing studies have shown that when TTA is updated with crafted adversarial test samples, the performance on benign samples can deteriorate. We propose an effective and realistic attack method that better produces poisoned samples without access to benign samples.
  arXiv  Detail & Related papers  (2024-10-07T01:29:19Z)
• Parameter Matching Attack: Enhancing Practical Applicability of Availability Attacks [8.225819874406238]
  We propose a novel availability approach termed Matching Attack (PMA) PMA is the first availability attack that works when only a portion of data can be perturbed. We show that PMA outperforms existing methods, achieving significant model performance degradation when a part of the training data is perturbed.
  arXiv  Detail & Related papers  (2024-07-02T17:15:12Z)
• Data Shapley in One Training Run [88.59484417202454]
  Data Shapley provides a principled framework for attributing data's contribution within machine learning contexts. Existing approaches require re-training models on different data subsets, which is computationally intensive. This paper introduces In-Run Data Shapley, which addresses these limitations by offering scalable data attribution for a target model of interest.
  arXiv  Detail & Related papers  (2024-06-16T17:09:24Z)
• MEAOD: Model Extraction Attack against Object Detectors [45.817537875368956]
  Model extraction attacks allow attackers to replicate a substitute model with comparable functionality to the victim model. We propose an effective attack method called MEAOD for object detection models. We achieve an extraction performance of over 70% under the given condition of a 10k query budget.
  arXiv  Detail & Related papers  (2023-12-22T13:28:50Z)
• Low-Cost High-Power Membership Inference Attacks [15.240271537329534]
  Membership inference attacks aim to detect if a particular data point was used in training a model. We design a novel statistical test to perform robust membership inference attacks with low computational overhead. RMIA lays the groundwork for practical yet accurate data privacy risk assessment in machine learning.
  arXiv  Detail & Related papers  (2023-12-06T03:18:49Z)
• SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack [18.998300969035885]
  Model extraction attacks fool the target model by generating adversarial examples on a substitute model. We propose a novel data-free model extraction method named SCME, which considers both the inter- and intra-class diversity in synthesizing fake data.
  arXiv  Detail & Related papers  (2023-10-15T10:41:45Z)
• Exploring Model Dynamics for Accumulative Poisoning Discovery [62.08553134316483]
  We propose a novel information measure, namely, Memorization Discrepancy, to explore the defense via the model-level information. By implicitly transferring the changes in the data manipulation to that in the model outputs, Memorization Discrepancy can discover the imperceptible poison samples. We thoroughly explore its properties and propose Discrepancy-aware Sample Correction (DSC) to defend against accumulative poisoning attacks.
  arXiv  Detail & Related papers  (2023-06-06T14:45:24Z)
• Membership Inference Attacks against Language Models via Neighbourhood Comparison [45.086816556309266]
  Membership Inference attacks (MIAs) aim to predict whether a data sample was present in the training data of a machine learning model or not. Recent work has demonstrated that reference-based attacks which compare model scores to those obtained from a reference model trained on similar data can substantially improve the performance of MIAs. We investigate their performance in more realistic scenarios and find that they are highly fragile in relation to the data distribution used to train reference models.
  arXiv  Detail & Related papers  (2023-05-29T07:06:03Z)
• Membership Inference Attacks against Synthetic Data through Overfitting Detection [84.02632160692995]
  We argue for a realistic MIA setting that assumes the attacker has some knowledge of the underlying data distribution. We propose DOMIAS, a density-based MIA model that aims to infer membership by targeting local overfitting of the generative model.
  arXiv  Detail & Related papers  (2023-02-24T11:27:39Z)
• 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)
• DaST: Data-free Substitute Training for Adversarial Attacks [55.76371274622313]
  We propose a data-free substitute training method (DaST) to obtain substitute models for adversarial black-box attacks. To achieve this, DaST utilizes specially designed generative adversarial networks (GANs) to train the substitute models. Experiments demonstrate the substitute models can achieve competitive performance compared with the baseline models.
  arXiv  Detail & Related papers  (2020-03-28T04:28:13Z)

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.