As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Due to its decentralized nature, Federated Learning (FL) lends itself to adversarial attacks in
the form of backdoors during training. The goal of a backdoor is to corrupt the performance …

This paper provides a comprehensive survey of techniques for testing machine learning
systems; Machine Learning Testing (ML testing) research. It covers 144 papers on testing …

With rapid progress and significant successes in a wide spectrum of applications, deep
learning is being applied in many safety-critical environments. However, deep neural …

Deep learning models have achieved high performance on many tasks, and thus have been
applied to many security-critical scenarios. For example, deep learning-based face …

We investigate a new method for injecting backdoors into machine learning models, based
on compromising the loss-value computation in the model-training code. We use it to …

As machine learning becomes widely used for automated decisions, attackers have strong
incentives to manipulate the results and models generated by machine learning algorithms …

Abstract Internet of Things (IoT) have opened the door to a world of unlimited possibilities for
implementations in varied sectors in society, but it also has many challenges. One of those …

This work provides the community with a timely comprehensive review of backdoor attacks
and countermeasures on deep learning. According to the attacker's capability and affected …