3 accepted papers
With the escalating complexity and investment cost of training deep neural networks safeguarding them from unauthorized usage and intellectual property theft has become imperative. Especially the rampant misuse of prediction APIs to replicate models without access to the original data or architectur…
Deep learning based face swapping technologies have opened new frontiers for entertainment industries while pose novel threats to identity security. Applying face swapping to real-world products, as well as defending against its misuse, rely on the capacity to generate high quality face swapped imag…
Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security sensitive applications. However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stea…