  Machine Vision Components (MVC) are becoming safety-critical. Assuring their
quality, including safety, is essential for their successful deployment.
Assurance relies on the availability of precisely specified and, ideally,
machine-verifiable requirements. MVCs with state-of-the-art performance rely on
machine learning (ML) and training data but largely lack such requirements.
  In this paper, we address the need for defining machine-verifiable
reliability requirements for MVCs against transformations that simulate the
full range of realistic and safety-critical changes in the environment. Using
human performance as a baseline, we define reliability requirements as: 'if the
changes in an image do not affect a human's decision, neither should they
affect the MVC's.' To this end, we provide: (1) a class of safety-related image
transformations; (2) reliability requirement classes to specify
correctness-preservation and prediction-preservation for MVCs; (3) a method to
instantiate machine-verifiable requirements from these requirements classes
using human performance experiment data; (4) human performance experiment data
for image recognition involving eight commonly used transformations, from about
2000 human participants; and (5) a method for automatically checking whether an
MVC satisfies our requirements. Further, we show that our reliability
requirements are feasible and reusable by evaluating our methods on 13
state-of-the-art pre-trained image classification models. Finally, we
demonstrate that our approach detects reliability gaps in MVCs that other
existing methods are unable to detect.
