Towards Adversarial Robustness via Transductive Learning

• URL: http://arxiv.org/abs/2106.08387v1
• Date: Tue, 15 Jun 2021 19:32:12 GMT
• Title: Towards Adversarial Robustness via Transductive Learning
• Authors: Jiefeng Chen, Yang Guo, Xi Wu, Tianqi Li, Qicheng Lao, Yingyu Liang, Somesh Jha
• Abstract summary: There has been emerging interest to use transductive learning for adversarial robustness. We first formalize and analyze modeling aspects of transductive robustness. We present new theoretical and empirical evidence in support of the utility of transductive learning.
• Score: 41.47295098415148
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: There has been emerging interest to use transductive learning for adversarial robustness (Goldwasser et al., NeurIPS 2020; Wu et al., ICML 2020). Compared to traditional "test-time" defenses, these defense mechanisms "dynamically retrain" the model based on test time input via transductive learning; and theoretically, attacking these defenses boils down to bilevel optimization, which seems to raise the difficulty for adaptive attacks. In this paper, we first formalize and analyze modeling aspects of transductive robustness. Then, we propose the principle of attacking model space for solving bilevel attack objectives, and present an instantiation of the principle which breaks previous transductive defenses. These attacks thus point to significant difficulties in the use of transductive learning to improve adversarial robustness. To this end, we present new theoretical and empirical evidence in support of the utility of transductive learning.

Related papers

• Adversarial Attacks and Defenses in Multivariate Time-Series Forecasting for Smart and Connected Infrastructures [0.9217021281095907]
  We investigate the impact of adversarial attacks on time-series forecasting. We employ untargeted white-box attacks to poison the inputs to the training process, effectively misleading the model. Having demonstrated the feasibility of these attacks, we develop robust models through adversarial training and model hardening.
  arXiv  Detail & Related papers  (2024-08-27T08:44:31Z)
• 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)
• Fast Preemption: Forward-Backward Cascade Learning for Efficient and Transferable Proactive Adversarial Defense [13.252842556505174]
  Deep learning technology has become untrustworthy due to its sensitivity to adversarial attacks. We have devised a proactive strategy that preempts by safeguarding media upfront. We have also devised the first, to our knowledge, effective white-box adaptive reversion attack.
  arXiv  Detail & Related papers  (2024-07-22T10:23:44Z)
• 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)
• Resisting Deep Learning Models Against Adversarial Attack Transferability via Feature Randomization [17.756085566366167]
  We propose a feature randomization-based approach that resists eight adversarial attacks targeting deep learning models. Our methodology can secure the target network and resists adversarial attack transferability by over 60%.
  arXiv  Detail & Related papers  (2022-09-11T20:14:12Z)
• Can Adversarial Training Be Manipulated By Non-Robust Features? [64.73107315313251]
  Adversarial training, originally designed to resist test-time adversarial examples, has shown to be promising in mitigating training-time availability attacks. We identify a novel threat model named stability attacks, which aims to hinder robust availability by slightly perturbing the training data. Under this threat, we find that adversarial training using a conventional defense budget $epsilon$ provably fails to provide test robustness in a simple statistical setting.
  arXiv  Detail & Related papers  (2022-01-31T16:25:25Z)
• Towards Evaluating the Robustness of Neural Networks Learned by Transduction [44.189248766285345]
  Greedy Model Space Attack (GMSA) is an attack framework that can serve as a new baseline for evaluating transductive-learning based defenses. We show that GMSA, even with weak instantiations, can break previous transductive-learning based defenses.
  arXiv  Detail & Related papers  (2021-10-27T19:39:50Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.