Abstract In this concept paper we explore the topic of Reversibility in Neural Networks leveraged for Network Integrity Verification and craft the original term Trusted Neural Network (TNN), paired with the API abstraction around it, to embrace the idea formally. This newly proposed high-level generalizable TNN, model builds upon the Invertible Neural Network architecture, trained simultaneously in both forward and reverse directions. This allows for the original system inputs to be compared with the ones reconstructed from the outputs in the reversed flow to assess the integrity of the end-to-end inference flow. The outcome of that assessment is captured as an Integrity Score and exposed as TNN output. Concrete implementation reflecting the needs of specific problem domains can be derived from this general approach and are demonstrated in the experiments. The model aspires to become a useful practice in drafting high level systems architectures which incorporate AI capabilities.
News / Achievements