  Adversarial attacks on Neural Network weights, such as the progressive
bit-flip attack (PBFA), can cause a catastrophic degradation in accuracy by
flipping a very small number of bits. Furthermore, PBFA can be conducted at run
time on the weights stored in DRAM main memory. In this work, we propose RADAR,
a Run-time adversarial weight Attack Detection and Accuracy Recovery scheme to
protect DNN weights against PBFA. We organize weights that are interspersed in
a layer into groups and employ a checksum-based algorithm on weights to derive
a 2-bit signature for each group. At run time, the 2-bit signature is computed
and compared with the securely stored golden signature to detect the bit-flip
attacks in a group. After successful detection, we zero out all the weights in
a group to mitigate the accuracy drop caused by malicious bit-flips. The
proposed scheme is embedded in the inference computation stage. For the
ResNet-18 ImageNet model, our method can detect 9.6 bit-flips out of 10 on
average. For this model, the proposed accuracy recovery scheme can restore the
accuracy from below 1% caused by 10 bit flips to above 69%. The proposed method
has extremely low time and storage overhead. System-level simulation on gem5
shows that RADAR only adds <1% to the inference time, making this scheme highly
suitable for run-time attack detection and mitigation.
