ObfusDomainNet: Reinforcement Learning-Driven Obfuscation and Domain-Locking for DNN Security

Protecting Deep Neural Network (DNN) in fields like medical image analysis is challenging due to unauthorized access and misuse, which traditional methods like watermarking fail to prevent. We propose a reinforcement learning-driven weight obfuscation and key protection mechanism, along with a domain-locked task-specific model generation framework, to enhance DNN authorization protection. The weight obfuscation creates dynamic masks, ensuring only authorized users can access model functions, rendering illegal copies ineffective. The domain-locked framework uses Generative Adversarial Network (GAN) to maximize performance differences between tasks, ensuring the model excels only on authorized tasks and preventing misuse. Additionally, we embed watermarks into model parameters with hash algorithms for tamper recovery, allowing restoration after attacks. Experimental results show these methods significantly enhance DNN security and reliability in authorization protection, applicability assurance, and tamper recovery, with minimal impact on performance.