SANAI: Secure ECC Distributed Keyed Parity Embedded in Weights for Protecting Neural Networks against Bit-Flip Attacks

Summary

SANAI is a sensitivity-guided, bit-plane-selective scheme that protects quantized neural network weights against bit flips with zero additional parity-storage overhead. It hides parity inside the weights using keyed embedding, with spread-transform dither modulation binding parity to a secret key and keyed interleaving dispersing concentrated attacks across codewords. Across ten vision backbones and ten language models from 82M to 13B parameters, it stays within about one percentage point of clean accuracy at bit-error rates up to 12%, twice the maximum of prior embedded-ECC approaches.

Mitigation

SANAI is itself the proposed mitigation: it verifies weights on the load path with a streaming checker on an 8x8 systolic array, at 0.41% area overhead and no additional memory.

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