Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers

• URL: http://arxiv.org/abs/2404.11665v1
• Date: Wed, 17 Apr 2024 18:03:12 GMT
• Title: Exploring DNN Robustness Against Adversarial Attacks Using Approximate Multipliers
• Authors: Mohammad Javad Askarizadeh, Ebrahim Farahmand, Jorge Castro-Godinez, Ali Mahani, Laura Cabrera-Quiros, Carlos Salazar-Garcia,
• Abstract summary: Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving. Their high computational complexity and vulnerability to adversarial attacks are ongoing challenges. 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.
• Score: 1.3820778058499328
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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