The Limits of SEMA on Distinguishing Similar Activation Functions of Embedded Deep Neural Networks

Abstract

Artificial intelligence (AI) is progressing rapidly, and in this trend, edge AI has been researched intensively. However, much less work has been performed around the security of edge AI. Machine learning models are a mass of intellectual property, and an optimized network is very valuable. Trained machine learning models need to be black boxes as well because they may give away information about the training data to the outside world. As selecting the appropriate activation functions to enable fast training of accurate deep neural networks is an active area of research, it is important to conceal the information of the activation functions used in a neural network architecture as well. There has been research on the use of physical attacks such as the side-channel attack (SCA) in areas other than cryptography. The SCA is highly effective against edge artificial intelligence due to its property of the device computing close to the user. We studied a previously proposed method to retrieve the activation functions of a black box neural network implemented on an edge device by using simple electromagnetic analysis (SEMA) and improved the signal processing procedure for further noisy measurements. The SEMA attack identifies activation functions by directly observing distinctive electromagnetic (EM) traces that correspond to the operations in the activation function. This method requires few executions and inputs and also has little implementation dependency on the activation functions. We distinguished eight similar activation functions with EM measurements and examined the versatility and limits of this attack. In this work, the machine learning architecture is a multilayer perceptron, evaluated on an Arduino Uno.

Keywords

• machine learning
• deep learning
• side-channel
• activation function
• SEMA