Backdoor attacks against neural networks have been intensively investigated, where the
adversary compromises the integrity of the victim model, causing it to make wrong …

Recent research shows deep neural networks are vulnerable to different types of attacks,
such as adversarial attacks, data poisoning attacks, and backdoor attacks. Among them …

Recently, backdoor attacks pose a new security threat to the training process of deep neural
networks (DNNs). Attackers intend to inject hidden backdoors into DNNs, such that the …

Recently, machine learning models have demonstrated to be vulnerable to backdoor
attacks, primarily due to the lack of transparency in black-box models such as deep neural …

Backdoor attacks are rapidly emerging threats to deep neural networks (DNNs). In the
backdoor attack scenario, attackers usually implant the backdoor into the target model by …

Recent works have demonstrated that deep learning models are vulnerable to backdoor
poisoning attacks, where these attacks instill spurious correlations to external trigger …

Backdoor attacks have been shown to be a serious threat against deep learning systems
such as biometric authentication and autonomous driving. An effective backdoor attack …

Backdoor attack is a type of serious security threat to deep learning models. An adversary
can provide users with a model trained on poisoned data to manipulate prediction behavior …

Although deep neural networks (DNNs) have achieved a great success in various computer
vision tasks, it is recently found that they are vulnerable to adversarial attacks. In this paper …

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 …