Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks

• URL: http://arxiv.org/abs/2307.06608v1
• Date: Thu, 13 Jul 2023 08:10:48 GMT
• Title: Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks
• Authors: Jiaming Zhang, Jitao Sang, Qi Yi
• Abstract summary: No-box adversarial attacks are becoming more practical and challenging for AI systems. This paper recasts adversarial attack as a downstream task by introducing foundational models as surrogate models.
• Score: 15.882687207499373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, the no-box adversarial attack, in which the attacker lacks access to the model's architecture, weights, and training data, become the most practical and challenging attack setup. However, there is an unawareness of the potential and flexibility inherent in the surrogate model selection process on no-box setting. Inspired by the burgeoning interest in utilizing foundational models to address downstream tasks, this paper adopts an innovative idea that 1) recasting adversarial attack as a downstream task. Specifically, image noise generation to meet the emerging trend and 2) introducing foundational models as surrogate models. Harnessing the concept of non-robust features, we elaborate on two guiding principles for surrogate model selection to explain why the foundational model is an optimal choice for this role. However, paradoxically, we observe that these foundational models underperform. Analyzing this unexpected behavior within the feature space, we attribute the lackluster performance of foundational models (e.g., CLIP) to their significant representational capacity and, conversely, their lack of discriminative prowess. To mitigate this issue, we propose the use of a margin-based loss strategy for the fine-tuning of foundational models on target images. The experimental results verify that our approach, which employs the basic Fast Gradient Sign Method (FGSM) attack algorithm, outstrips the performance of other, more convoluted algorithms. We conclude by advocating for the research community to consider surrogate models as crucial determinants in the effectiveness of adversarial attacks in no-box settings. The implications of our work bear relevance for improving the efficacy of such adversarial attacks and the overall robustness of AI systems.

Related papers

• On Transfer-based Universal Attacks in Pure Black-box Setting [94.92884394009288]
  We study the role of prior knowledge of the target model data and number of classes in attack performance. We also provide several interesting insights based on our analysis, and demonstrate that priors cause overestimation in transferability scores.
  arXiv  Detail & Related papers  (2025-04-11T10:41:20Z)
• Model Privacy: A Unified Framework to Understand Model Stealing Attacks and Defenses [11.939472526374246]
  This work presents a framework called Model Privacy'', providing a foundation for comprehensively analyzing model stealing attacks and defenses. We propose methods to quantify the goodness of attack and defense strategies, and analyze the fundamental tradeoffs between utility and privacy in ML models.
  arXiv  Detail & Related papers  (2025-02-21T16:29:11Z)
• Transferable Adversarial Attacks on SAM and Its Downstream Models [87.23908485521439]
  This paper explores the feasibility of adversarial attacking various downstream models fine-tuned from the segment anything model (SAM) To enhance the effectiveness of the adversarial attack towards models fine-tuned on unknown datasets, we propose a universal meta-initialization (UMI) algorithm.
  arXiv  Detail & Related papers  (2024-10-26T15:04:04Z)
• A Hybrid Defense Strategy for Boosting Adversarial Robustness in Vision-Language Models [9.304845676825584]
  We propose a novel adversarial training framework that integrates multiple attack strategies and advanced machine learning techniques. Experiments conducted on real-world datasets, including CIFAR-10 and CIFAR-100, demonstrate that the proposed method significantly enhances model robustness.
  arXiv  Detail & Related papers  (2024-10-18T23:47:46Z)
• Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks [11.389689242531327]
  Adversarial training is one of the most effective methods for enhancing model robustness. Previous approaches primarily use static ground truth for adversarial training, but this often causes robust overfitting. We propose a dynamic label adversarial training (DYNAT) algorithm that enables the target model to gain robustness from the guide model's decisions.
  arXiv  Detail & Related papers  (2024-08-23T14:25:12Z)
• MirrorCheck: Efficient Adversarial Defense for Vision-Language Models [55.73581212134293]
  We propose a novel, yet elegantly simple approach for detecting adversarial samples in Vision-Language Models. Our method leverages Text-to-Image (T2I) models to generate images based on captions produced by target VLMs. Empirical evaluations conducted on different datasets validate the efficacy of our approach.
  arXiv  Detail & Related papers  (2024-06-13T15:55:04Z)
• Defending Large Language Models Against Attacks With Residual Stream Activation Analysis [0.0]
  Large Language Models (LLMs) are vulnerable to adversarial threats. This paper presents an innovative defensive strategy, given white box access to an LLM. We apply a novel methodology for analyzing distinctive activation patterns in the residual streams for attack prompt classification.
  arXiv  Detail & Related papers  (2024-06-05T13:06:33Z)
• Data Poisoning for In-context Learning [49.77204165250528]
  In-context learning (ICL) has been recognized for its innovative ability to adapt to new tasks. This paper delves into the critical issue of ICL's susceptibility to data poisoning attacks. We introduce ICLPoison, a specialized attacking framework conceived to exploit the learning mechanisms of ICL.
  arXiv  Detail & Related papers  (2024-02-03T14:20:20Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• Effective Backdoor Mitigation in Vision-Language Models Depends on the Pre-training Objective [71.39995120597999]
  Modern machine learning models are vulnerable to adversarial and backdoor attacks. Such risks are heightened by the prevalent practice of collecting massive, internet-sourced datasets for training multimodal models. CleanCLIP is the current state-of-the-art approach to mitigate the effects of backdooring in multimodal models.
  arXiv  Detail & Related papers  (2023-11-25T06:55:13Z)
• Defense Against Model Extraction Attacks on Recommender Systems [53.127820987326295]
  We introduce Gradient-based Ranking Optimization (GRO) to defend against model extraction attacks on recommender systems. GRO aims to minimize the loss of the protected target model while maximizing the loss of the attacker's surrogate model. Results show GRO's superior effectiveness in defending against model extraction attacks.
  arXiv  Detail & Related papers  (2023-10-25T03:30:42Z)
• OMG-ATTACK: Self-Supervised On-Manifold Generation of Transferable Evasion Attacks [17.584752814352502]
  Evasion Attacks (EA) are used to test the robustness of trained neural networks by distorting input data. We introduce a self-supervised, computationally economical method for generating adversarial examples. Our experiments consistently demonstrate the method is effective across various models, unseen data categories, and even defended models.
  arXiv  Detail & Related papers  (2023-10-05T17:34:47Z)
• Practical Membership Inference Attacks Against Large-Scale Multi-Modal Models: A Pilot Study [17.421886085918608]
  Membership inference attacks (MIAs) aim to infer whether a data point has been used to train a machine learning model. These attacks can be employed to identify potential privacy vulnerabilities and detect unauthorized use of personal data. This paper takes a first step towards developing practical MIAs against large-scale multi-modal models.
  arXiv  Detail & Related papers  (2023-09-29T19:38:40Z)
• Enhancing Adversarial Attacks: The Similar Target Method [6.293148047652131]
  adversarial examples pose a threat to deep neural networks' applications. Deep neural networks are vulnerable to adversarial examples, posing a threat to the models' applications and raising security concerns. We propose a similar targeted attack method named Similar Target(ST)
  arXiv  Detail & Related papers  (2023-08-21T14:16:36Z)
• Delving into Data: Effectively Substitute Training for Black-box Attack [84.85798059317963]
  We propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack.
  arXiv  Detail & Related papers  (2021-04-26T07:26:29Z)
• Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack [13.28881502612207]
  In some scenarios, AI models are trained proprietarily, where neither pre-trained models nor sufficient in-distribution data is publicly available. We find the effectiveness of existing techniques significantly affected by the absence of pre-trained models. We formulate model extraction attacks into an adaptive framework that captures these factors with deep reinforcement learning.
  arXiv  Detail & Related papers  (2021-04-13T03:46:59Z)
• On the model-based stochastic value gradient for continuous reinforcement learning [50.085645237597056]
  We show that simple model-based agents can outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward. Our findings suggest that model-based policy evaluation deserves closer attention.
  arXiv  Detail & Related papers  (2020-08-28T17:58:29Z)
• Query-Free Adversarial Transfer via Undertrained Surrogates [14.112444998191698]
  We introduce a new method for improving the efficacy of adversarial attacks in a black-box setting by undertraining the surrogate model which the attacks are generated on. We show that this method transfers well across architectures and outperforms state-of-the-art methods by a wide margin.
  arXiv  Detail & Related papers  (2020-07-01T23:12:22Z)
• Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution [83.02632136860976]
  We study black-box adversarial attacks against deep neural networks (DNNs) We develop a novel mechanism of adversarial transferability, which is robust to the surrogate biases. Experiments on benchmark datasets and attacking against real-world API demonstrate the superior attack performance of the proposed method.
  arXiv  Detail & Related papers  (2020-06-15T16:45:27Z)

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.