Abstract Model quantization is widely used to compress and accelerate deep neural
networks. However recent studies have revealed the feasibility of weaponizing model …

Backdoors on federated learning will be diluted by subsequent benign updates. This is
reflected in the significant reduction of attack success rate as iterations increase, ultimately …

Recent advances in artificial intelligence (AI) have sparked interest in developing
explainable AI (XAI) methods for clinical decision support systems, especially in translational …

Machine-Learning-as-a-Service (MLaaS) is an emerging product to meet the market
demand. However, end users are required to upload data to the remote server when using …

Backdoor attacks in reinforcement learning (RL) have previously employed intense attack
strategies to ensure attack success. However, these methods suffer from high attack costs …

Adversarial machine learning (AML) studies the adversarial phenomenon of machine
learning, which may make inconsistent or unexpected predictions with humans. Some …

Model Inversion Attacks (MIAs) pose a certain threat to the data privacy of learning-based
systems, as they enable adversaries to reconstruct identifiable features of the training …

Deep neural network (DNN) models have been proven vulnerable to backdoor attacks. One
trend of backdoor attacks is developing more invisible and dynamic triggers to make attacks …

The backdoor attacks have posed a severe threat to deep neural networks (DNNs). Online
training platforms and third-party model training providers are more vulnerable to backdoor …

The backdoor attack has emerged as a prevalent threat that affects the effectiveness of
machine learning models in intelligent vehicles. While such attacks may not impair the …