Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection

• URL: http://arxiv.org/abs/2311.04588v1
• Date: Wed, 8 Nov 2023 10:31:29 GMT
• Title: Army of Thieves: Enhancing Black-Box Model Extraction via Ensemble based sample selection
• Authors: Akshit Jindal, Vikram Goyal, Saket Anand, Chetan Arora
• Abstract summary: In Model Stealing Attacks (MSA), a machine learning model is queried repeatedly to build a labelled dataset. In this work, we explore the usage of an ensemble of deep learning models as our thief model. We achieve a 21% higher adversarial sample transferability than previous work for models trained on the CIFAR-10 dataset.
• Score: 10.513955887214497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine Learning (ML) models become vulnerable to Model Stealing Attacks (MSA) when they are deployed as a service. In such attacks, the deployed model is queried repeatedly to build a labelled dataset. This dataset allows the attacker to train a thief model that mimics the original model. To maximize query efficiency, the attacker has to select the most informative subset of data points from the pool of available data. Existing attack strategies utilize approaches like Active Learning and Semi-Supervised learning to minimize costs. However, in the black-box setting, these approaches may select sub-optimal samples as they train only one thief model. Depending on the thief model's capacity and the data it was pretrained on, the model might even select noisy samples that harm the learning process. In this work, we explore the usage of an ensemble of deep learning models as our thief model. We call our attack Army of Thieves(AOT) as we train multiple models with varying complexities to leverage the crowd's wisdom. Based on the ensemble's collective decision, uncertain samples are selected for querying, while the most confident samples are directly included in the training data. Our approach is the first one to utilize an ensemble of thief models to perform model extraction. We outperform the base approaches of existing state-of-the-art methods by at least 3% and achieve a 21% higher adversarial sample transferability than previous work for models trained on the CIFAR-10 dataset.

Related papers

• Pandora's White-Box: Precise Training Data Detection and Extraction in Large Language Models [4.081098869497239]
  We develop state-of-the-art privacy attacks against Large Language Models (LLMs) New membership inference attacks (MIAs) against pretrained LLMs perform hundreds of times better than baseline attacks. In fine-tuning, we find that a simple attack based on the ratio of the loss between the base and fine-tuned models is able to achieve near-perfect MIA performance.
  arXiv  Detail & Related papers  (2024-02-26T20:41:50Z)
• Beyond Labeling Oracles: What does it mean to steal ML models? [52.63413852460003]
  Model extraction attacks are designed to steal trained models with only query access. We investigate factors influencing the success of model extraction attacks. Our findings urge the community to redefine the adversarial goals of ME attacks.
  arXiv  Detail & Related papers  (2023-10-03T11:10:21Z)
• Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model [14.834360664780709]
  Model attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models. This paper develops a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.
  arXiv  Detail & Related papers  (2023-07-17T12:14:24Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks [86.55317144826179]
  Previous methods always leverage the transferable adversarial examples as the model fingerprint. We propose a novel yet simple model stealing detection method based on SAmple Correlation (SAC) SAC successfully defends against various model stealing attacks, even including adversarial training or transfer learning.
  arXiv  Detail & Related papers  (2022-10-21T02:07:50Z)
• MEGA: Model Stealing via Collaborative Generator-Substitute Networks [4.065949099860426]
  Recent data-free model stealingmethods are shown effective to extract the knowledge of thetarget model without using real query examples. We propose a data-free model stealing frame-work,MEGA, which is based on collaborative generator-substitute networks. Our results show that theaccuracy of our trained substitute model and the adversarialattack success rate over it can be up to 33% and 40% higherthan state-of-the-art data-free black-box attacks.
  arXiv  Detail & Related papers  (2022-01-31T09:34:28Z)
• Defending against Model Stealing via Verifying Embedded External Features [90.29429679125508]
  adversaries can steal' deployed models even when they have no training samples and can not get access to the model parameters or structures. We explore the defense from another angle by verifying whether a suspicious model contains the knowledge of defender-specified emphexternal features. Our method is effective in detecting different types of model stealing simultaneously, even if the stolen model is obtained via a multi-stage stealing process.
  arXiv  Detail & Related papers  (2021-12-07T03:51:54Z)
• Knowledge-Enriched Distributional Model Inversion Attacks [49.43828150561947]
  Model inversion (MI) attacks are aimed at reconstructing training data from model parameters. We present a novel inversion-specific GAN that can better distill knowledge useful for performing attacks on private models from public data. Our experiments show that the combination of these techniques can significantly boost the success rate of the state-of-the-art MI attacks by 150%.
  arXiv  Detail & Related papers  (2020-10-08T16:20:48Z)
• 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.