DeepHammer: Depleting the Intelligence of Deep Neural Networks through Targeted Chain of Bit Flips

Understand

Security of machine learning is increasingly becoming a major concern due to the ubiquitous deployment of deep learning in many security-sensitive domains.

• Many prior studies have shown external attacks such as adversarial examples that tamper with the integrity of DNNs using maliciously crafted inputs.
• However, the security implication of internal threats (i.e., hardware vulnerability) to DNN models has not yet been well understood.
• In this paper, we demonstrate the first hardware-based attack on quantized deep neural networks-DeepHammer-that deterministically induces bit flips in model weights to compromise DNN inference by exploiting the rowhammer vulnerability.