Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions

Abstract

Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at test time. Although backdoor attacks have been demonstrated in a variety of settings and against different models, the factors affecting their effectiveness are still not well understood. In this work, we provide a unifying framework to study the process of backdoor learning under the lens of incremental learning and influence functions. We show that the effectiveness of backdoor attacks depends on: (i) the complexity of the learning algorithm, controlled by its hyperparameters; (ii) the fraction of backdoor samples injected into the training set; and (iii) the size and visibility of the backdoor trigger. These factors affect how fast a model learns to correlate the presence of the backdoor trigger with the target class. Our analysis unveils the intriguing existence of a region in the hyperparameter space in which the accuracy on clean test samples is still high while backdoor attacks are ineffective, thereby suggesting novel criteria to improve existing defenses.

arXiv commentPreprint; Paper accepted at International Journal of Machine Learning and Cybernetics; 25 pages

References in corpus (6)

• Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning
• A high-bias, low-variance introduction to Machine Learning for physicists
• Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
• On the Effectiveness of Mitigating Data Poisoning Attacks with Gradient Shaping
• Machine Learning Security against Data Poisoning: Are We There Yet?
• Baseline Pruning-Based Approach to Trojan Detection in Neural Networks

Cited by in corpus (1)

• Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning