Designing Trojan Detectors in Neural Networks Using Interactive Simulations

Published

February 20, 2021

Author(s)

Peter Bajcsy, Nicholas J. Schaub, Michael P. Majurski

Abstract

This paper addresses the problem of designing trojan detectors in neural networks (NNs) using interactive simulations. Trojans in NNs are defined as triggers in inputs that cause misclassification of such inputs into a class (or classes) unintended by the design of a NN-based model. The goal of our work is to understand encodings of a variety of trojan types in fully connected layers of neural networks. Our approach is (1) to simulate nine types of trojan embeddings into dot patterns, (2) to devise measurements of NN states, and (3) to design trojan detectors in NN-based classification models. The interactive simulations are built on top of TensorFlow Playground with in-memory storage of data and NN coefficients. The simulations provide analytical, visualization, and output operations performed on training datasets and NN architectures. The measurements of a NN include (a) model inefficiency using modified Kullback-Liebler (KL) divergence from uniformly distributed states and (b) model sensitivity to variables related to data and NNs. Using the KL divergence measurements at each NN layer and per each predicted class label, a trojan detector is devised to discriminate NN models with or without trojans. To document robustness of such a trojan detector with respect to NN architectures, dataset perturbations, and trojan types, several properties of the KL divergence measurement are presented. For the general use, the web-based simulations is deployed via GitHub pages at https://github.com/usnistgov/nn-calculator.

Citation

Applied Sciences

Volume

11

Issue

4

Pub Type

Journals

Download Paper

https://dx.doi.org/10.3390/app11041865

Local Download

Keywords

artificial intelligence, neural networks, security, Trojan detection

Citation

Bajcsy, P. , Schaub, N. and Majurski, M. (2021), Designing Trojan Detectors in Neural Networks Using Interactive Simulations, Applied Sciences, [online], https://dx.doi.org/10.3390/app11041865 (Accessed November 12, 2024)