DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks

• URL: http://arxiv.org/abs/2405.13891v2
• Date: Sun, 2 Jun 2024 08:23:56 GMT
• Title: DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks
• Authors: Patrik Velčický, Jakub Breier, Mladen Kovačević, Xiaolu Hou,
• Abstract summary: We introduce an encoding-based protection method against bit-flip attacks on neural networks, titled DeepNcode. Our results show an increase in protection margin of up to $7.6times$ for $4-$bit and $12.4times$ for $8-$bit quantized networks.
• Score: 4.734824660843964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model extraction, and trojan/backdoor planting. Most of these attacks work by flipping bits in the memory where quantized model parameters are stored. In this paper, we introduce an encoding-based protection method against bit-flip attacks on neural networks, titled DeepNcode. We experimentally evaluate our proposal with several publicly available models and datasets, by using state-of-the-art bit-flip attacks: BFA, T-BFA, and TA-LBF. Our results show an increase in protection margin of up to $7.6\times$ for $4-$bit and $12.4\times$ for $8-$bit quantized networks. Memory overheads start at $50\%$ of the original network size, while the time overheads are negligible. Moreover, DeepNcode does not require retraining and does not change the original accuracy of the model.

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