5 accepted papers
Deep neural network watermarking is an emerging technique for protecting the copyright of models. Most existing black-box watermarking methods leverage the backdoor, making them inherently vulnerable to backdoor removal attacks. In this paper, we propose a novel watermark removal attack, Misleading…
Model watermarking secures ownership verification and copyright protection of deep neural networks. In the black-box scenario, watermarking schemes commonly rely on injecting triggers and requiring the model's training data to maintain its performance. However, such knowledge might be unavailable in…
As a modern ensemble technique, Deep Forest (DF) employs a cascading structure to construct deep models, providing stronger representational power compared to traditional decision forests. However, its greedy multi-layer learning procedure is prone to overfitting, limiting model effectiveness and ge…
Backdoor-based watermarking schemes were proposed to protect the intellectual property of deep neural networks under the black-box setting. However, additional security risks emerge after the schemes have been published for as forensics tools. This paper reveals the capsulation attack that can easil…
The wide application of deep learning techniques is boosting the regulation of deep learning models, especially deep neural networks (DNN), as commercial products. A necessary prerequisite for such regulations is identifying the owner of deep neural networks, which is usually done through the waterm…