Towards Class-Oriented Poisoning Attacks Against Neural Networks

@article{Zhao2020TowardsCP,
  title={Towards Class-Oriented Poisoning Attacks Against Neural Networks},
  author={Bingyin Zhao and Yingjie Lao},
  journal={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
  year={2020},
  pages={2244-2253}
}
  • Bingyin ZhaoYingjie Lao
  • Published 31 July 2020
  • Computer Science
  • 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Poisoning attacks on machine learning systems compromise the model performance by deliberately injecting malicious samples in the training dataset to influence the training process. Prior works focus on either availability attacks (i.e., lowering the overall model accuracy) or integrity attacks (i.e., enabling specific instance based backdoor). In this paper, we advance the adversarial objectives of the availability attacks to a per-class basis, which we refer to as class-oriented poisoning… 
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4 Citations

CLPA: Clean-Label Poisoning Availability Attacks Using Generative Adversarial Nets

A clean-label approach, CLPA, for the poisoning availability attack is proposed and it is revealed that due to the intrinsic imperfection of classifiers, naturally misclassified inputs can be considered as a special type of poisoned data, which is referred to as "natural poisoned data''.

A Survey of Neural Trojan Attacks and Defenses in Deep Learning

A comprehensive review of the techniques that devise Trojan attacks for deep learning and explore their defenses, and provides a comprehensible gateway to the broader community to understand the recent developments in Neural Trojans.

Defending Evasion Attacks via Adversarially Adaptive Training

A novel adversarially adaptive defense (AAD) framework based on adaptive training such that the trained prediction and detection models adapt at test time to new attacks.

Triggerability of Backdoor Attacks in Multi-Source Transfer Learning-based Intrusion Detection

Backdoor attacks on multis-source transfer learning models are feasible, although having less impact compared to backdoors on traditional machine learning models.

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