The proliferation of Deep Neural Networks (DNN) in commercial applications is expanding rapidly. Simultaneously, the increasing complexity and cost of training DNN models have intensified the urgency surrounding the protection of intellectual property associated with these trained models. In this regard, DNN watermarking has emerged as a crucial safeguarding technique. This paper presents FedReverse, a novel multiparty reversible watermarking approach for robust copyright protection while minimizing performance impact. Unlike existing methods, FedReverse enables collaborative watermark embedding from multiple parties after model training, ensuring individual copyright claims. In addition, FedReverse is reversible, enabling complete watermark removal with unanimous client consent. FedReverse demonstrates perfect covering, ensuring that observations of watermarked content do not reveal any information about the hidden watermark. Additionally, it showcases resistance against Known Original Attacks (KOA), making it highly challenging for attackers to forge watermarks or infer the key. This paper further evaluates FedReverse through comprehensive simulations involving Multi-layer Perceptron (MLP) and Convolutional Neural Networks (CNN) trained on the MNIST dataset. The simulations demonstrate FedReverse's robustness, reversibility, and minimal impact on model accuracy across varying embedding parameters and multiple client scenarios.

该论文介绍了FedReverse，一种用于鲁棒版权保护且影响性能最小化的新型多方逆向水印嵌入方法。与现有方法不同，FedReverse可以在模型训练后进行多方协作水印嵌入，确保个人版权声明，并可通过一致的用户同意完全去除水印。FedReverse具有完美覆盖性，保证水印隐藏不会透露任何信息，并展示了对已知原始攻击（KOA）的抵抗力，使攻击者难以伪造水印或推断出密钥。该论文通过在MNIST数据集上训练的多层感知器（MLP）和卷积神经网络（CNN）进行了综合模拟评估，展示了FedReverse在不同嵌入参数和多客户场景下的鲁棒性、可逆性和对模型准确性的最小影响。

FedReverse: 多方可逆深度神经网络数字水印