Robustness of Deep Learning Perception Models

深度学习感知模型的鲁棒性

• 批准号: 2579432
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2579432
• 关键词: Robustness; Deep; Learning; Perception; Models

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.

EPSRC领域：人工智能技术，人工智能和机器人主题，验证和纠正公司参与：丰田欧洲简要描述研究的背景，包括潜在的影响：深度学习(DL)近年来有了巨大的进步，导致该技术在现实世界的应用中得到了广泛的应用，包括人脸识别和自动驾驶，但感知模型可能是不稳定的WRT对手示例，其中输入的微小修改会导致错误分类。为了确保应用的安全性和安全性，需要严格的方法来促进开发健壮的感知模型，这些模型可以整合到机器人应用的自动控制器中。评估DL模型的稳健性的典型方法是通过对对抗性示例的启发式搜索(例如，基于梯度的随机搜索)，这不能保证如果找不到对抗性示例就不存在。另一种更强大的替代方法是使用自动验证，其目的是为给定场景中的模型行为提供可证明的保证。虽然最近在这方面取得了很大进展，但重点主要是关于简单输入操作的局部稳健性，并且缺乏支持自然几何变换和自动驾驶应用中典型的上下文效果的框架。数字图书馆感知模型的成功部署关键取决于能够证明它们对各种自然和上下文转换的稳健性。这个项目将开发新的方法来为DL感知模型提供这样的语义健壮性保证。目的和目标：在这个研究计划中，我们将建立在PI开发的深度学习技术(例如，深度神经网络的安全验证，CAV 2017)及其对贝叶斯神经网络的扩展(例如，贝叶斯神经网络的概率安全，UAI 2020)的基础上，为评估适于包括在自动车辆和机器人控制器中的3D感知模型的健壮性奠定理论基础。这项研究将由具体的应用场景和感知框架提供信息，并将利用一系列符号和神经技术，包括贝叶斯学习、不确定性量化、约束求解和因果推理。重点将放在调查研究问题和制定与实际应用相关的框架上。研究方法的新颖性：本项目将关注表示学习和语义健壮性之间的交集。大多数以前的工作都集中在有界像素扰动下的分类稳健性。我们假设语义稳健性是一个更强的稳健性条件，它不仅要求输入的标签预测在扰动下是稳定的，而且要求输入的中间表示在语义扰动下是稳定的。因此，我们将专注于表示学习的稳健性，特别是对于复杂的几何变换和上下文变化。