Robustness of Deep Learning Perception Models

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EPSRC areas: Artificial intelligence technologies, Artificial intelligence and robotics theme, Verification and correctnessCompany involved: Toyota EuropeBrief description of the context of the research including potential impact:Deep learning (DL) has advanced dramatically in recent years, resulting in widespread take up of the technology in real-world applications, including face recognition and autonomous driving, but perception models can be unstable wrt adversarial examples, where a small modification to input causes a misclassification. To ensure safety and security of applications, rigorous methodologies are needed that facilitate the development of robust perception models that can be incorporated within automated controllers for robotic applications. A typical approach to evaluating robustness of DL models is through heuristic search for adversarial examples (e.g., gradient-based, stochastic search), which offers no guarantees that adversarial examples do not exist if not found. An alternative, more powerful, method is to employ automated verification, which aims to provide provable guarantees on the model behaviour in a given scenario. While there has been much progress recently in this direction, the focus has been mainly on local robustness with respect to simple input manipulations, and there is a lack of frameworks that support natural geometric transformations and contextual effects that are typical in autonomous driving applications. Successful deployment of DL perception models crucially depends on the ability to provably guarantee their robustness against a wide range of natural and contextual transformations. This project will develop new methods for providing such semantic robustness guarantees for DL perception models.Aims and Objectives:In this research programme we will build on the techniques developed by the PI for deep learning (e.g. Safety Verification of Deep Neural Networks, CAV 2017) and their extension to Bayesian neural networks (e.g. Probabilistic Safety for Bayesian Neural Networks, UAI 2020) to develop the theoretical foundations for evaluating robustness of 3D perception models that are amenable to inclusion within automated vehicle and robotic controllers. The research will be informed by concrete application scenarios and sensing frameworks, and will draw on a range of symbolic and neural techniques, including Bayesian learning, uncertainty quantification, constraint solving and causal reasoning. The focus will be on investigating research questions and developing frameworks that are relevant for practical applications. Novelty of the research methodology:This project will focus on the intersection between representation learning and semantic robustness. Most prior work has focused on classification robustness under bounded pixel perturbations. We posit that semantic robustness is a stronger robustness condition that requires not only stable label predictions under perturbations but also that the intermediate representations of an input are stable under semantic perturbations. Therefore, we will focus on the robustness of representation learning, particularly as regarding complex geometric transformations and contextual changes.