Holistic Adversarial Robustness of Deep Learning Models

• URL: http://arxiv.org/abs/2202.07201v1
• Date: Tue, 15 Feb 2022 05:30:27 GMT
• Title: Holistic Adversarial Robustness of Deep Learning Models
• Authors: Pin-Yu Chen and Sijia Liu
• Abstract summary: Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models.
• Score: 91.34155889052786
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability. With the proliferation of deep-learning based technology, the potential risks associated with model development and deployment can be amplified and become dreadful vulnerabilities. This paper provides a comprehensive overview of research topics and foundational principles of research methods for adversarial robustness of deep learning models, including attacks, defenses, verification, and novel applications.

Related papers

• New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook [54.24701201956833]
  Security and privacy issues have undermined users' confidence in pre-trained models. Current literature lacks a clear taxonomy of emerging attacks and defenses for pre-trained models. This taxonomy categorizes attacks and defenses into No-Change, Input-Change, and Model-Change approaches.
  arXiv  Detail & Related papers  (2024-11-12T10:15:33Z)
• Robust Image Classification: Defensive Strategies against FGSM and PGD Adversarial Attacks [0.0]
  Adversarial attacks pose significant threats to the robustness of deep learning models in image classification. This paper explores and refines defense mechanisms against these attacks to enhance the resilience of neural networks.
  arXiv  Detail & Related papers  (2024-08-20T02:00:02Z)
• Impact of Architectural Modifications on Deep Learning Adversarial Robustness [16.991522358940774]
  We present an experimental evaluation of the effects of model modifications on deep learning model robustness using adversarial attacks. Our results indicate the pressing demand for an in-depth assessment of the effects of model changes on the robustness of models.
  arXiv  Detail & Related papers  (2024-05-03T08:58:38Z)
• Analyzing Adversarial Inputs in Deep Reinforcement Learning [53.3760591018817]
  We present a comprehensive analysis of the characterization of adversarial inputs, through the lens of formal verification. We introduce a novel metric, the Adversarial Rate, to classify models based on their susceptibility to such perturbations. Our analysis empirically demonstrates how adversarial inputs can affect the safety of a given DRL system with respect to such perturbations.
  arXiv  Detail & Related papers  (2024-02-07T21:58:40Z)
• It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness [4.1310970179750015]
  Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to confuse the model into making a mistake. To address this problem, the area of adversarial robustness investigates mechanisms behind adversarial attacks and defenses against these attacks.
  arXiv  Detail & Related papers  (2023-03-17T04:18:03Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
  We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
  arXiv  Detail & Related papers  (2022-11-27T21:43:45Z)
• Poisoning Attacks and Defenses on Artificial Intelligence: A Survey [3.706481388415728]
  Data poisoning attacks represent a type of attack that consists of tampering the data samples fed to the model during the training phase, leading to a degradation in the models accuracy during the inference phase. This work compiles the most relevant insights and findings found in the latest existing literatures addressing this type of attacks. A thorough assessment is performed on the reviewed works, comparing the effects of data poisoning on a wide range of ML models in real-world conditions.
  arXiv  Detail & Related papers  (2022-02-21T14:43:38Z)
• Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications [27.033174829788404]
  This tutorial aims to introduce the fundamentals of adversarial robustness of deep learning. We will highlight state-of-the-art techniques in adversarial attacks and robustness verification of deep neural networks (DNNs) We will also introduce some effective countermeasures to improve the robustness of deep learning models.
  arXiv  Detail & Related papers  (2021-08-24T00:08:33Z)
• ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models [64.03398193325572]
  Inference attacks against Machine Learning (ML) models allow adversaries to learn about training data, model parameters, etc. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models.
  arXiv  Detail & Related papers  (2021-02-04T11:35:13Z)

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.