Wicked Oddities: Selectively Poisoning for Effective Clean-Label Backdoor Attacks
This work addresses a practical threat in machine learning security, particularly for systems using third-party datasets, by enabling more effective attacks with limited information, though it is incremental as it builds on prior sample selection methods.
The paper tackles the problem of clean-label backdoor attacks by proposing selective poisoning strategies for a threat model where the attacker only provides data for the target class and lacks knowledge of the victim model or other classes, resulting in improved attack success rates as demonstrated in experiments on benchmark datasets.
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.