Illegitimate reproduction, distribution and derivation of Deep Neural Network (DNN) models can inflict economic loss, reputation damage and even privacy infringement. Passive DNN intellectual property (IP) protection methods such as watermarking and fingerprinting attempt to prove the ownership upon IP violation, but they are often too late to stop catastrophic damage of IP abuse and too feeble against strong adversaries. In this paper, we propose IDEA, an Inverse Domain Expert Adaptation based proactive DNN IP protection method featuring active authorization and source traceability. IDEA generalizes active authorization as an inverse problem of domain adaptation. The multi-adaptive optimization is solved by a mixture-of-experts model with one real and two fake experts. The real expert re-optimizes the source model to correctly classify test images with a unique model user key steganographically embedded. The fake experts are trained to output random prediction on test images without or with incorrect user key embedded by minimizing their mutual information (MI) with the real expert. The MoE model is knowledge distilled into a unified protected model to avoid leaking the expert model features by maximizing their MI with additional multi-layer attention and contrastive representation loss optimization. IDEA not only prevents unauthorized users without the valid key to access the functional model, but also enable the model owner to validate the deployed model and trace the source of IP infringement. We extensively evaluate IDEA on five datasets and four DNN models to demonstrate its effectiveness in authorization control, culprit tracing success rate, and robustness against various attacks.

翻译：深度神经网络（DNN）模型的非法复制、分发与衍生可导致经济损失、声誉损害甚至隐私侵犯。被动式DNN知识产权（IP）保护方法（如数字水印与指纹识别）试图在侵权行为发生后证明所有权，但往往无法及时阻止IP滥用造成的灾难性损害，且在强大攻击者面前显得脆弱。本文提出IDEA，一种基于逆向领域专家适应的主动式DNN知识产权保护方法，具备主动授权与来源追溯能力。IDEA将主动授权问题泛化为领域适应的逆向问题，通过包含一个真实专家与两个伪造专家的混合专家模型求解多适应优化问题。真实专家通过隐写方式嵌入唯一用户密钥，对源模型进行重优化以正确分类测试图像；两个伪造专家则通过最小化其与真实专家的互信息，被训练为在未嵌入密钥或嵌入错误密钥时对测试图像输出随机预测。该混合专家模型通过最大化互信息，并结合多层注意力机制与对比表征损失优化，被知识蒸馏为统一的受保护模型，从而避免泄露专家模型特征。IDEA不仅能阻止未持有有效密钥的未授权用户访问功能模型，还使模型所有者能够验证部署模型并追溯侵权来源。我们在五个数据集和四种DNN模型上对IDEA进行了广泛评估，证明了其在授权控制、侵权溯源成功率及抵御各类攻击的鲁棒性方面的有效性。