Robust Binary Neural Network Defending Against Bit-Flip Attacks
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Solution Overview
Problem
Deep neural networks (DNNs) are vulnerable to adversarial weight attacks, such as bit-flip attacks, which compromise their performance with minimal model parameter perturbation, and existing defense mechanisms trade off clean inference accuracy for improved robustness.
Innovation Solution
A robust and accurate binary neural network (RA-BNN) is developed, utilizing complete binary weights and activations, combined with a novel two-stage network growing method called early growth, to enhance resistance to bit-flip attacks while maintaining clean inference accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete binary neural network is used to defend against bit-flip attacks, then robustness to adversarial weight attacks is improved, but clean inference accuracy deteriorates
Solution Approach 1:
The patent applies local quality by differentiating the binarization strategy across different layers of the neural network. Specifically, it binarizes weights and activations in intermediate layers while maintaining non-binary representations in the input and output layers. This selective binarization allows the network to gain robustness from binary operations in hidden layers while preserving accuracy through non-binary operations at critical input/output interfaces.
Solution Approach 2:
The patent segments the neural network into distinct functional zones with different precision requirements. By dividing the network into intermediate layers (which use binary operations for robustness) and boundary layers (which use non-binary operations for accuracy), it creates a segmented architecture that optimizes for both robustness and accuracy simultaneously.
2Reliability
If low bit-width binary neural network is used, then resistance to bit-flip attacks is improved, but clean model accuracy deteriorates
Solution Approach 1:
The patent implements local quality by applying different bit-widths to different components of the network. Intermediate layer activations and weights use 1-bit representation for robustness, while input and output layer activations maintain higher precision (non-binary) representations for accurate inference. This local differentiation resolves the contradiction between using low bit-width for robustness and high bit-width for accuracy.
Data Source
AI summary
A robust and accurate binary neural network, referred to as RA-BNN, is provided to simultaneously defend against adversarial noise injection and improve accuracy. Recently developed adversarial weight attack, a.k.a. bit-flip attack (BFA), has shown enormous success in compromising deep neural network (DNN) performance with an extremely small amount of model parameter perturbation. To defend against this threat, embodiments of RA-BNN adopt a complete binary neural network (BNN) to significantly improve DNN model robustness (defined as the number of bit-flips required to degrade the accuracy to as low as a random guess). To improve clean inference accuracy, a novel and efficient two-stage network growing method is proposed and referred to as early growth. Early growth selectively grows the channel size of each BNN layer based on channel-wise binary masks training with Gumbel-Sigmoid function. Apart from recovering the inference accuracy, the RA-BNN after growing also shows significantly higher resistance to BFA.