SHF: Medium: More Reliable Image Networks through Scene-based Specification, Neuro-symbolic Training, and Systematic Specification-driven Testing

SHF：中：通过基于场景的规范、神经符号训练和系统规范驱动测试实现更可靠的图像网络

• 批准号: 2312487
• 负责人: Sebastian Elbaum
• 金额: $ 117.47万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312487&HistoricalAwards=false
• 关键词: SHF; Medium; More; Reliable; Image

Deep Neural Networks (DNN) are becoming an essential part of safety-critical autonomous systems, from automobiles to medical devices. Failures in such safety-critical autonomous systems may lead to injury or loss of life. Although there are mature techniques for improving the accuracy of DNNs, such techniques do not provide guarantees that the behavior of a DNN will always be appropriate. Without such guarantees the deployment of DNNs in safety and mission critical systems will be limited or unnecessarily risky. This project seeks to assure the quality of image-based DNNs through the development of techniques that change two fundamental current practices: 1) the specification of desirable DNN properties will be abstracted from the pixel-level to domain entities (e.g., people, cars) to enable reasoning about the correctness of DNN behaviors, and 2) the application of those properties will pervade the DNN development process so that the resulting DNNs behave in accordance with those properties. If successful, the research will improve assurance of systems that include DNNs and, thereby, the safety of the public. Modern image Deep Neural Networks can be extremely complex accepting high-resolution images and processing them through many dozens of layers with tens of millions of parameters to compute outputs. Methods of assessing and improving the statistical accuracy of computed outputs relative to labeled training data are in regular use, but such methods provide no guarantees that the behavior of the DNN will be appropriate, especially on unusual or rare inputs. This project seeks to establish the foundations, algorithms and engineering advances for a new approach to developing image-based DNNs with behavior guarantees. The project shifts the direction from prior research that has focused on reasoning about limited forms of DNN correctness at the pixel level, such as local robustness, and instead aims to enable the specification of higher-level properties that abstract from pixel-level variation to describe equivalence classes of behavior and then to incorporate such specifications through the training, testing, and deployment of DNNs. The project activities include developing: 1) a high-level symbolic method for specifying necessary correctness properties of pixel-based DNNs; 2) methods to incorporate such specifications into the training of DNNs so as to guarantee their specification conformance; and 3) methods to assess and improve training, test, and validation sets to ensure that they adequately represent important, but rare, inputs and thereby enable DNNs to generalize to such inputs. Collectively, this work will establish the first high-level approach to specifying the intended behavior of image DNNs and, if successful, the project will provide a foundation for building more reliable DNN-enabled systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

深度神经网络（DNN）已成为从汽车到医疗设备的安全至关重要自治系统的重要组成部分。这种关键的自治系统中的失败可能导致伤害或生命损失。尽管有提高DNN准确性的成熟技术，但这种技术不能保证DNN的行为始终是合适的。 不用这样的保证，将在安全和任务关键系统中部署DNN将是有限或不必要的风险。该项目旨在通过开发改变两种基本当前实践的技术来确保基于图像的DNN的质量：1）可观的DNN属性的规范将从像素级别到域实体（例如，人们，汽车）从像素级实体到域名（例如，汽车）进行抽象，以启用有关DNN行为的正确性，以及该属性的正确性，以及2）属性的应用程序，以及2）属性，以及2）属性。 DNN按照这些属性行事。 如果成功，该研究将提高包括DNN在内的系统的保证，从而提高公众的安全。现代图像深度神经网络可能非常复杂，可以接受高分辨率图像并通过数十层进行处理，并具有数千万参数来计算输出。 评估和提高相对于标记培训数据的计算输出的统计准确性的方法是定期使用的，但是这种方法不能保证DNN的行为是否合适，尤其是在异常或罕见的输入上。 该项目旨在为一种新方法建立基础，算法和工程进步，以开发具有行为保证的基于图像的DNN。该项目将方向从先前的研究转变为重点是在像素级别（例如局部鲁棒性）上推理有限形式的DNN正确性，而是旨在使高级属性的规范从像素级别的变化中抽象出来，以描述等效类别的行为类别，然后通过训练，测试，测试，DNN和DNN的部署来结合此类规格。项目活动包括开发：1）一种高级符号方法，用于指定基于像素的DNN的必要正确性属性； 2）将此类规格纳入DNN的培训以保证其规范符合性的方法； 3）评估和改善培训，测试和验证集以确保它们充分代表重要但罕见的输入的方法，从而使DNN能够概括为此类投入。总的来说，这项工作将建立第一种高级方法来指定图像DNN的预期行为，如果成功的话，该项目将为建立更可靠的DNN支持系统提供基础。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子和更广泛的影响，可以通过评估来进行评估，以评估Criteria。