Due to their computational efficiency and speed during training and inference, extreme learning machines are suitable for simple learning tasks on lightweight datasets. Examples of their real-world applications include healthcare and edge devices, where security concerns are crucial to be examined. Backdoor attacks are among the most common security threats against machine learning models but are almost completely unexplored for extreme learning machines.

This paper investigates the effects of backdoor attacks on extreme learning machines. First, we inject the backdoor into the model through data and model poisoning and then examine the pruning technique as a defense to defend against the attack. The core characteristic of extreme learning machines, which makes them interesting for study, is their different structure and learning procedure compared to deep neural networks. These features raise the question of whether they are as vulnerable to backdoor attacks as deep neural networks. Our experiments confirm this assumption and indicate that extreme learning machines can be backdoored with 100% attack success rate. Thus, we believe further study is needed to develop a robust defense technique as a solution to make them less vulnerable.