Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks

Nguyen Hung-Quang, Ngoc-Hieu Nguyen, The-Anh Ta, Thanh Nguyen-Tang, Kok-Seng Wong, Hoang Thanh-Tung, Khoa D Doan

Keywords: backdoor attack, data selection

TL;DR: We propose a strategy that select data to poison to improve the success rate of clean label backdoor attacks.

Abstract:

Deep neural networks are vulnerable to backdoor attacks, a type of adversarial attack that poisons the training data to manipulate the behavior of models trained on such data.
Clean-label attacks are a more stealthy form of backdoor attacks that can perform the attack without changing the labels of poisoned data.
Early works on clean-label attacks added triggers to a random subset of the training set, ignoring the fact that samples contribute unequally to the attack's success. This results in high poisoning rates and low attack success rates.
To alleviate the problem, several supervised learning-based sample selection strategies have been proposed.
However, these methods assume access to the entire labeled training set and require training, which is expensive and may not always be practical.
This work studies a new and more practical (but also more challenging) threat model where the attacker only provides data for the target class (e.g., in face recognition systems) and has no knowledge of the victim model or any other classes in the training set.
We study different strategies for selectively poisoning a small set of training samples in the target class to boost the attack success rate in this setting.
Our threat model poses a serious threat in training machine learning models with third-party datasets, since the attack can be performed effectively with limited information. Experiments on benchmark datasets illustrate the effectiveness of our strategies in improving clean-label backdoor attacks.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.

Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2025/AuthorGuide.

Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.

No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.

Submission Number: 11375