  This work addresses the problem of planting and defending cryptographic-based
backdoors in artificial intelligence (AI) models. The motivation comes from our
lack of understanding and the implications of using cryptographic techniques
for planting undetectable backdoors under theoretical assumptions in the large
AI model systems deployed in practice. Our approach is based on designing a
web-based simulation playground that enables planting, activating, and
defending cryptographic backdoors in neural networks (NN). Simulations of
planting and activating backdoors are enabled for two scenarios: in the
extension of NN model architecture to support digital signature verification
and in the modified architectural block for non-linear operators. Simulations
of backdoor defense against backdoors are available based on proximity analysis
and provide a playground for a game of planting and defending against
backdoors. The simulations are available at
https://pages.nist.gov/nn-calculator
