The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …

The success of machine learning is fueled by the increasing availability of computing power
and large training datasets. The training data is used to learn new models or update existing …

While recent works have indicated that federated learning (FL) may be vulnerable to
poisoning attacks by compromised clients, their real impact on production FL systems is not …

Abstract Machine learning are vulnerable to the threats. The Intruders can utilize the
malicious nature of the nodes to attack the training dataset to worsen the process and …

Federated learning (FL) is an emerging paradigm for distributed training of large-scale deep
neural networks in which participants' data remains on their own devices with only model …

Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at
training time. A backdoor attack installs a backdoor into the victim model by injecting a …

In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …

This survey paper discusses opportunities and threats of using artificial intelligence (AI)
technology in the manufacturing sector with consideration for offensive and defensive uses …

The decentralized nature of federated learning makes detecting and defending against
adversarial attacks a challenging task. This paper focuses on backdoor attacks in the …

With the success of deep learning algorithms in various domains, studying adversarial
attacks to secure deep models in real world applications has become an important research …