DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks
<|reference_start|>DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks: 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.<|reference_end|>
@article{velčický2024deepncode:,
    title={DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural
  Networks},
    author={Patrik Velv{c}ick'y, Jakub Breier, Mladen Kovav{c}evi'c, Xiaolu
  Hou},
    journal={arXiv preprint arXiv:2405.13891},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.13891},
    primaryClass={cs.CR cs.AI}
}