Deep Learning is the most widely used tool in the contemporary field of computer vision. Its
ability to accurately solve complex problems is employed in vision research to learn deep …

Deep learning technology is increasingly being applied in safety-critical scenarios but has
recently been found to be susceptible to imperceptible adversarial perturbations. This raises …

Backdoor attack has emerged as a major security threat to deep neural networks (DNNs).
While existing defense methods have demonstrated promising results on detecting or …

Backdoor attack intends to embed hidden backdoors into deep neural networks (DNNs), so
that the attacked models perform well on benign samples, whereas their predictions will be …

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 …

Deep learning is at the heart of the current rise of artificial intelligence. In the field of
computer vision, it has become the workhorse for applications ranging from self-driving cars …

Backdoor attacks are serious security threats to machine learning models where an
adversary can inject poisoned samples into the training set, causing a backdoored model …

Deep Learning is currently used to perform multiple tasks, such as object recognition, face
recognition, and natural language processing. However, Deep Neural Networks (DNNs) are …

The tremendous amount of accessible data in cyberspace face the risk of being
unauthorized used for training deep learning models. To address this concern, methods are …

With the growing demand for the right to be forgotten, there is an increasing need for
machine learning models to forget sensitive data and its impact. To address this, the …