Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-node Attack

Key points

• With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection.1
• However, when edge devices are compromised, attackers can obtain white-box access to the deployed models by reversing the apps directly to obtain the knowledge of peer devices, which is known as Cross-Node Attacks, like performing efficient adversarial attacks.1
• Motivated by findings that the model robustness is primarily influenced by the curvature of the decision boundary, we investigate how model parameters shape its curvature.1
• Across all four major types of adversarial attacks with four widely-deployed on-device models, BMO achieves an average success rate of 68.68% in defense, far exceeding the most competitive baseline, which attains only 22.32% in defending against cross-node attacks.1

Sources (1)

• [1]Mind Your Boundaries: Boundary-Aware Model Obfuscation against Cross-node Attack
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 06:33 PM

  In this paper, we propose a novel method, BMO, that obfuscates model parameters to diversify the decision boundary further resisting cross-node attack while preserving its benign accuracy.

  With the advancement of powerful AI chips, on-device machine learning is gaining traction for its low latency and privacy protection.

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