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.

Recently, attackers have targeted machine learning systems, introducing various attacks. The backdoor attack is popular in this field and is usually realized through data poisoning. To the best of our knowledge, we are the first to investigate whether the backdoor attacks remain effective when manifold learning algorithms are applied to the poisoned dataset. We conducted our experiments using two manifold learning techniques (Autoencoder and UMAP) on two benchmark datasets (MNIST and CIFAR10) and two backdoor strategies (clean and dirty label). We performed an array of experiments using different parameters, finding that we could reach an attack success rate of 95% and 75% even after reducing our data to two dimensions using Autoencoders and UMAP, respectively.

Deep learning found its place in various real-world applications, where many also have security requirements. Unfortunately, as these systems become more pervasive, understanding how they fail becomes more challenging. While there are multiple failure modes in machine learning, one category received significant attention in the last few years-backdoor attacks. Backdoor attacks aim to make a model misclassify some of its inputs to a preset-specific label while other classification results would behave normally. While many works investigate various backdoor attacks and defenses for different domains, no works aim to provide a systematic comparison of backdoor attacks for different scenarios. This work considers backdoor attacks in image, sound, text, and graph domains and provides a comparative analysis of their respective strengths.