AI Model Utilization Measurements For Finding Class Encoding Patterns
- URL: http://arxiv.org/abs/2212.06576v1
- Date: Mon, 12 Dec 2022 02:18:10 GMT
- Title: AI Model Utilization Measurements For Finding Class Encoding Patterns
- Authors: Peter Bajcsy and Antonio Cardone and Chenyi Ling and Philippe Dessauw
and Michael Majurski and Tim Blattner and Derek Juba and Walid Keyrouz
- Abstract summary: This work addresses the problems of designing utilization measurements of trained artificial intelligence (AI) models.
The problems are motivated by the lack of explainability of AI models in security and safety critical applications.
- Score: 2.702380921892937
- License: http://creativecommons.org/publicdomain/zero/1.0/
- Abstract: 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.
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