AI Model Utilization Measurements For Finding Class Encoding Patterns
It addresses the lack of explainability in AI models for security-critical applications like self-driving cars, but is incremental as it builds on existing concepts of model analysis.
This work tackled the problem of designing utilization measurements for trained AI models to explain how training data are encoded, using traffic sign classification in self-driving cars as a case study, and found that these measurements can reveal patterns in class encodings for both clean and poisoned models, with implications for trojan detection.
This work addresses the problems of (a) designing utilization measurements of trained artificial intelligence (AI) models and (b) explaining how training data are encoded in AI models based on those measurements. The problems are motivated by the lack of explainability of AI models in security and safety critical applications, such as the use of AI models for classification of traffic signs in self-driving cars. We approach the problems by introducing theoretical underpinnings of AI model utilization measurement and understanding patterns in utilization-based class encodings of traffic signs at the level of computation graphs (AI models), subgraphs, and graph nodes. Conceptually, utilization is defined at each graph node (computation unit) of an AI model based on the number and distribution of unique outputs in the space of all possible outputs (tensor-states). In this work, utilization measurements are extracted from AI models, which include poisoned and clean AI models. In contrast to clean AI models, the poisoned AI models were trained with traffic sign images containing systematic, physically realizable, traffic sign modifications (i.e., triggers) to change a correct class label to another label in a presence of such a trigger. We analyze class encodings of such clean and poisoned AI models, and conclude with implications for trojan injection and detection.