This work presents a web-based interactive neural network (NN) calculator and
a NN inefficiency measurement that has been investigated for the purpose of
detecting trojans embedded in NN models. This NN Calculator is designed on top
of TensorFlow Playground with in-memory storage of data and NN graphs plus
coefficients. It is "like a scientific calculator" with analytical,
visualization, and output operations performed on training datasets and NN
architectures. The prototype is aaccessible at
this https URL The analytical capabilities include a
novel measurement of NN inefficiency using modified Kullback-Liebler (KL)
divergence applied to histograms of NN model states, as well as a
quantification of the sensitivity to variables related to data and NNs. Both NN
Calculator and KL divergence are used to devise a trojan detector approach for
a variety of trojan embeddings. Experimental results document desirable
properties of the KL divergence measurement with respect to NN architectures
and dataset perturbations, as well as inferences about embedded trojans.

设计了一种基于 TensorFlow Playground 的在线神经网络（NN）计算器和一种用于探测嵌入式晶体管的 NN 效率测量方法，通过使用修改后的 Kullback-Liebler 散度应用于 NN 模型状态的直方图，同时量化与数据和 NN 相关的变量的敏感性，开发了一种基于 NN 计算器和 KL 散度的 Trojan 检测方法，并通过实验结果验证了其可行性和高效性。