论文标题
深锁：深神经网络的安全授权
Deep-Lock: Secure Authorization for Deep Neural Networks
论文作者
论文摘要
训练有素的深神经网络（DNN）模型在几种商业模型中被认为是有价值的知识产权（IP）。行业的重大关注点已经提出了预防IP盗窃和未经授权使用此类DNN模型的使用。在本文中，我们通过提出一种通用且轻巧的基于密钥的模型锁定方案来解决防止未经授权使用DNN模型的问题，该模型方案可确保仅在应用正确的秘密密钥时才能正确锁定模型。所提出的方案（称为深锁）利用具有良好安全属性的S框来对经过训练的DNN模型的每个参数进行加密，并通过通过键调度算法从主密钥生成的秘密键来加密训练有素的DNN模型。与模型微调攻击相比，发现了加密权重的致密网络。最后，深锁不需要在DNN模型的结构和培训中进行任何干预，这使其适用于DNN的所有现有软件和硬件实现。
Trained Deep Neural Network (DNN) models are considered valuable Intellectual Properties (IP) in several business models. Prevention of IP theft and unauthorized usage of such DNN models has been raised as of significant concern by industry. In this paper, we address the problem of preventing unauthorized usage of DNN models by proposing a generic and lightweight key-based model-locking scheme, which ensures that a locked model functions correctly only upon applying the correct secret key. The proposed scheme, known as Deep-Lock, utilizes S-Boxes with good security properties to encrypt each parameter of a trained DNN model with secret keys generated from a master key via a key scheduling algorithm. The resulting dense network of encrypted weights is found robust against model fine-tuning attacks. Finally, Deep-Lock does not require any intervention in the structure and training of the DNN models, making it applicable for all existing software and hardware implementations of DNN.
