A novel model watermarking for protecting generative adversarial network

In this paper, we introduce DSN (Deep Serial Number), a new watermarking approach that can prevent the stolen model from being deployed by unauthorized parties. Recently, watermarking in DNNs has emerged as a new research direction for owners to claim ownership of DNN models. However, the verification schemes of existing watermarking approaches are vulnerable to various watermark attacks. Different from existing work that embeds identification information into DNNs, we explore a new DNN Intellectual Property Protection mechanism that can prevent adversaries from deploying the stolen deep neural networks. Motivated by the success of serial number in protecting conventional software IP, we introduce the first attempt to embed a serial number into DNNs. Specifically, the proposed DSN is implemented in the knowledge distillation framework, where a private teacher DNN is first trained, then its knowledge is distilled and transferred to a series of customized student DNNs. During the distillation process, each customer DNN is augmented with a unique serial number, i.e., an encrypted 0/1 bit trigger pattern. Customer DNN works properly only when a potential customer enters the valid serial number. The embedded serial number could be used as a strong watermark for ownership verification. Experiments on various applications indicate that DSN is effective in terms of preventing unauthorized application while not sacrificing the original DNN performance. The experimental analysis further shows that DSN is resistant to different categories of attacks.

pdf/deep-serial-number-computational-watermarking-for-dnn-8dyicdmtjv.pdf

Deep Serial Number: Computational Watermarking for DNN Intellectual Property Protection.

The widespread adoption of Artificial Intelligence and Machine Learning tools opens to security issues that can raise and occur when the underlying ML models integrated into advanced services. The models, in fact, can be compromised in both the learning and the deployment stage. In this work, we provide an overview of some strenuous security risks and concerns that can affect such models. Our focus is on the research challenges and defense opportunities of the underlying ML framework, when it is devised in specific contexts that can compromise its effectiveness. Specifically, the survey provides an overview of the following emerging topics: Model Watermarking, Information Hiding issues and defense opportunities, Adversarial Learning and model robustness, and Fairness-aware models. 

Emerging challenges and perspectives in Deep Learning model security: A brief survey

Federated learning (FL) is a technique that allows multiple participants to collaboratively train a Deep Neural Network (DNN) without the need to centralize their data. Among other advantages, it comes with privacy-preserving properties, making it attractive for application in sensitive contexts, such as health care or the military. Although the data are not explicitly exchanged, the training procedure requires sharing information about participants’ models. This makes the individual models vulnerable to theft or unauthorized distribution by malicious actors. To address the issue of ownership rights protection in the context of machine learning (ML), DNN watermarking methods have been developed during the last five years. Most existing works have focused on watermarking in a centralized manner, but only a few methods have been designed for FL and its unique constraints. In this paper, we provide an overview of recent advancements in federated learning watermarking, shedding light on the new challenges and opportunities that arise in this field. 

When Federated Learning Meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection

Federated Learning (FL) is a technique that allows multiple participants to collaboratively train a Deep Neural Network (DNN) without the need of centralizing their data. Among other advantages, it comes with privacy-preserving properties making it attractive for application in sensitive contexts, such as health care or the military. Although the data are not explicitly exchanged, the training procedure requires sharing information about participants' models. This makes the individual models vulnerable to theft or unauthorized distribution by malicious actors. To address the issue of ownership rights protection in the context of Machine Learning (ML), DNN Watermarking methods have been developed during the last five years. Most existing works have focused on watermarking in a centralized manner, but only a few methods have been designed for FL and its unique constraints. In this paper, we provide an overview of recent advancements in Federated Learning watermarking, shedding light on the new challenges and opportunities that arise in this field. 

pdf/when-federated-learning-meets-watermarking-a-comprehensive-5czsiw62bj.pdf

When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protection

Since machine learning models have become a valuable asset for companies, watermarking techniques have been developed to protect the intellectual property of these models and prevent model theft. We observe that current watermarking frameworks solely target image classification tasks, neglecting a considerable part of machine learning techniques. In this paper, we propose to address this lack and study the watermarking process of various machine learning techniques such as machine translation, regression, binary image classification and reinforcement learning models. We adapt current definitions to each specific technique and we evaluate the main characteristics of the watermarking process, in particular the robustness of the models against a rational adversary. We show that watermarking models beyond classification is possible while preserving their overall performance. We further investigate various attacks and discuss the importance of the performance metric in the verification process and its impact on the success of the adversary.

Yes We can: Watermarking Machine Learning Models beyond Classification

With the increasing development of machine learning models in daily businesses, a strong need for intellectual property protection arised. For this purpose, current works suggest to leverage backdoor techniques to embed a watermark into the model, by overfitting to a set of particularly crafted and secret input-output pairs called triggers. By sending verification queries containing triggers, the model owner can analyse the behavior of any suspect model on the queries to claim its ownership. However, when it comes to scenarios where frequent monitoring is needed, the computational overhead of these verification queries in terms of volume demonstrates that backdoor-based watermarking appears to be too sensitive to outlier detection attacks and cannot guarantee the secrecy of the triggers. To solve this issue, we introduce BlindSpot, to watermark machine learning models through fairness. Our trigger-less approach is compatible with a high number of verification queries while being robust to outlier detection attacks. We show on Fashion-MNIST and CIFAR-10 datasets that BlindSpot is efficiently watermarking models while robust to outlier detection attacks, at a performance cost on the accuracy of 2%.

BlindSpot: Watermarking Through Fairness

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Yes We can: Watermarking Machine Learning Models beyond Classification

BlindSpot: Watermarking Through Fairness