%0 Journal Article %1 jasper2024dnnshield %A Stang, Jasper %A Krauß, Torsten %A Dmitrienko, Alexandra %D 2024 %J arXiv:2403.06581 %K jasperstang myown sss-group torstenkrauss %T DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification %U https://arxiv.org/abs/2403.06581 %X The surge in popularity of machine learning (ML) has driven significant investments in training Deep Neural Networks (DNNs). However, these models that require resource-intensive training are vulnerable to theft and unauthorized use. This paper addresses this challenge by introducing DNNShield, a novel approach for DNN protection that integrates seamlessly before training. DNNShield embeds unique identifiers within the model architecture using specialized protection layers. These layers enable secure training and deployment while offering high resilience against various attacks, including fine-tuning, pruning, and adaptive adversarial attacks. Notably, our approach achieves this security with minimal performance and computational overhead (less than 5% runtime increase). We validate the effectiveness and efficiency of DNNShield through extensive evaluations across three datasets and four model architectures. This practical solution empowers developers to protect their DNNs and intellectual property rights.

@article{jasper2024dnnshield, abstract = {The surge in popularity of machine learning (ML) has driven significant investments in training Deep Neural Networks (DNNs). However, these models that require resource-intensive training are vulnerable to theft and unauthorized use. This paper addresses this challenge by introducing DNNShield, a novel approach for DNN protection that integrates seamlessly before training. DNNShield embeds unique identifiers within the model architecture using specialized protection layers. These layers enable secure training and deployment while offering high resilience against various attacks, including fine-tuning, pruning, and adaptive adversarial attacks. Notably, our approach achieves this security with minimal performance and computational overhead (less than 5% runtime increase). We validate the effectiveness and efficiency of DNNShield through extensive evaluations across three datasets and four model architectures. This practical solution empowers developers to protect their DNNs and intellectual property rights.}, added-at = {2024-03-12T21:43:47.000+0100}, author = {Stang, Jasper and Krauß, Torsten and Dmitrienko, Alexandra}, biburl = {https://www.bibsonomy.org/bibtex/2e71a7161e2e341067c783b6a130ca095/sssgroup}, interhash = {150c33ff6f8b0f17132b6fad0e1f9687}, intrahash = {e71a7161e2e341067c783b6a130ca095}, journal = {arXiv:2403.06581}, keywords = {jasperstang myown sss-group torstenkrauss}, month = mar, timestamp = {2024-04-23T14:14:14.000+0200}, title = {DNNShield: Embedding Identifiers for Deep Neural Network Ownership Verification}, url = {https://arxiv.org/abs/2403.06581}, year = 2024 }