DNNShield: Dynamic Randomized Model Sparsification, A Defense Against Adversarial Machine Learning. (arXiv:2208.00498v1 [cs.CR])

DNNs are known to be vulnerable to so-called adversarial attacks that
manipulate inputs to cause incorrect results that can be beneficial to an
attacker or damaging to the victim. Recent works have proposed approximate
computation as a defense mechanism against machine learning attacks. We show
that these approaches, while successful for a range of inputs, are insufficient
to address stronger, high-confidence adversarial attacks. To address this, we
propose DNNSHIELD, a hardware-accelerated defense that adapts the strength of
the response to …

adversarial machine learning arxiv defense learning machine machine learning