AdversarialStyle: GAN Based Style Guided Verification Framework for Deep Learning Systems

Abstract

Verification and validation of deep learning algorithms is an important and challenging topic of artificial intelligence. Without approving by reliable and rigorous verification methods, deep learning algorithms, for instance, the convolutional neural networks, are not qualified to be used in real-world applications, especially in safety-critical areas. The gap between deep learning systems and the requirements in safety-critical application areas, such as autonomous robotics and self-driving vehicles, is the lack of Black-box V&V techniques that can test and evaluate the performance and the robustness of deep learning systems. To bridge this gap, we proposed a GAN based Black-box verification framework called AdversarialStyle for generating and searching adversarial examples in both targeted and non-targeted way from different styles or domains of interest. The AdversarialStyle can not only evaluate deep learning models but also can discover the robustness level of every instance in the test set. Therefore, this framework can support deep learning model designers to understand and to explore their algorithms and improve the trustworthiness of AI techniques.