Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective

• URL: http://arxiv.org/abs/2302.09457v2
• Date: Thu, 4 Jan 2024 13:43:16 GMT
• Title: Attacks in Adversarial Machine Learning: A Systematic Survey from the Life-cycle Perspective
• Authors: Baoyuan Wu, Zihao Zhu, Li Liu, Qingshan Liu, Zhaofeng He, Siwei Lyu
• Abstract summary: Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans. Some paradigms have been recently developed to explore this adversarial phenomenon occurring at different stages of a machine learning system. We propose a unified mathematical framework to covering existing attack paradigms.
• Score: 69.25513235556635
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Adversarial machine learning (AML) studies the adversarial phenomenon of machine learning, which may make inconsistent or unexpected predictions with humans. Some paradigms have been recently developed to explore this adversarial phenomenon occurring at different stages of a machine learning system, such as backdoor attack occurring at the pre-training, in-training and inference stage; weight attack occurring at the post-training, deployment and inference stage; adversarial attack occurring at the inference stage. However, although these adversarial paradigms share a common goal, their developments are almost independent, and there is still no big picture of AML. In this work, we aim to provide a unified perspective to the AML community to systematically review the overall progress of this field. We firstly provide a general definition about AML, and then propose a unified mathematical framework to covering existing attack paradigms. According to the proposed unified framework, we build a full taxonomy to systematically categorize and review existing representative methods for each paradigm. Besides, using this unified framework, it is easy to figure out the connections and differences among different attack paradigms, which may inspire future researchers to develop more advanced attack paradigms. Finally, to facilitate the viewing of the built taxonomy and the related literature in adversarial machine learning, we further provide a website, \ie, \url{http://adversarial-ml.com}, where the taxonomies and literature will be continuously updated.

Related papers

• Unified Generative and Discriminative Training for Multi-modal Large Language Models [88.84491005030316]
  Generative training has enabled Vision-Language Models (VLMs) to tackle various complex tasks. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval. This paper proposes a unified approach that integrates the strengths of both paradigms.
  arXiv  Detail & Related papers  (2024-11-01T01:51:31Z)
• 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)
• Inference Attacks: A Taxonomy, Survey, and Promising Directions [44.290208239143126]
  This survey provides an in-depth and comprehensive inference of attacks and corresponding countermeasures in ML-as-a-service. We first propose the 3MP taxonomy based on the community research status, trying to normalize the confusing naming system of inference attacks. Also, we analyze the pros and cons of each type of inference attack, their workflow, countermeasure, and how they interact with other attacks.
  arXiv  Detail & Related papers  (2024-06-04T07:06:06Z)
• Defenses in Adversarial Machine Learning: A Survey [46.41995115842852]
  Adversarial phenomenon has been widely observed in machine learning (ML) systems, especially in those using deep neural networks. Several advanced attack paradigms have been developed to explore it, mainly including backdoor attacks, weight attacks, and adversarial examples. Various defense paradigms have been developed to improve the model robustness against the corresponding attack paradigm. This survey aims to build a systematic review of all existing defense paradigms from a unified perspective.
  arXiv  Detail & Related papers  (2023-12-13T15:42:55Z)
• Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks [5.860289498416911]
  Large Language Models (LLMs) are swiftly advancing in architecture and capability. As they integrate more deeply into complex systems, the urgency to scrutinize their security properties grows. This paper surveys research in the emerging interdisciplinary field of adversarial attacks on LLMs.
  arXiv  Detail & Related papers  (2023-10-16T21:37:24Z)
• Visual Adversarial Examples Jailbreak Aligned Large Language Models [66.53468356460365]
  We show that the continuous and high-dimensional nature of the visual input makes it a weak link against adversarial attacks. We exploit visual adversarial examples to circumvent the safety guardrail of aligned LLMs with integrated vision. Our study underscores the escalating adversarial risks associated with the pursuit of multimodality.
  arXiv  Detail & Related papers  (2023-06-22T22:13:03Z)
• Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning [1.6574413179773757]
  adversarial attacks aim to trick ML models into producing faulty predictions. adversarial attacks can compromise ML-based NIDSs. Our experiments indicate that continuous re-training, even without adversarial training, can reduce the effectiveness of adversarial attacks.
  arXiv  Detail & Related papers  (2023-06-08T18:32:08Z)
• MAML is a Noisy Contrastive Learner [72.04430033118426]
  Model-agnostic meta-learning (MAML) is one of the most popular and widely-adopted meta-learning algorithms nowadays. We provide a new perspective to the working mechanism of MAML and discover that: MAML is analogous to a meta-learner using a supervised contrastive objective function. We propose a simple but effective technique, zeroing trick, to alleviate such interference.
  arXiv  Detail & Related papers  (2021-06-29T12:52:26Z)
• Adversarial Machine Learning: Bayesian Perspectives [0.4915744683251149]
  Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting machine learning (ML) systems against security threats. In certain scenarios there may be adversaries that actively manipulate input data to fool learning systems. This creates a new class of security vulnerabilities that ML systems may face, and a new desirable property called adversarial robustness essential to trust operations.
  arXiv  Detail & Related papers  (2020-03-07T10:30:43Z)

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.