Exploring DNN Robustness Against Adversarial Attacks Using Approximate
  Multipliers
Mohammad Javad Askarizadeh Ebrahim Farahmand Jorge Castro-Godinez Ali Mahani Laura Cabrera-Quiros Carlos Salazar-Garcia
Deep Neural Networks (DNNs) have advanced in many real-world applications,
such as healthcare and autonomous driving. However, their high computational
complexity and vulnerability to adversarial attacks are ongoing challenges. In
this letter, approximate multipliers are used to explore DNN robustness
improvement against adversarial attacks. By uniformly replacing accurate
multipliers for state-of-the-art approximate ones in DNN layer models, we
explore the DNNs robustness against various adversarial attacks in a feasible
time. Results show up to 7% accuracy drop due to approximations when no attack
is present while improving robust accuracy up to 10% when attacks applied.