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
this https URL

在这篇论文中，研究了在人工智能模型中植入和防御基于密码学的后门的问题，并通过设计了一个基于网络的模拟平台，模拟了在神经网络中植入、激活和防御密码学后门的场景，以及通过接近度分析提供了一种植入和防御后门的游戏模拟。