Digital modelling is becoming more prevalent in many applications. The underlying 3D mesh objects are therefore getting increasingly valuable, such that methods for ownership protection are required. Watermarking is a solution to this problem, yet the research combining watermarking and 3D mesh objects is limited. This work aims to improve an existing watermarking method that embeds watermarking bits into a 3D model by selecting features, or the vertices with a larger mean curvature. We propose an improvement where a centre of mass calculation is changed to a centre of volume, aiming to achieve better robustness against mesh simplification attacks on 3D mesh objects by use of this volume centre instead of a mass centre. The proposed method of applying the watermark reaches an accuracy score that is 15.78 percentage points higher on average than the original method, when attacked by mesh simplification. Therefore, this work shows a more robust approach to the method build upon.

In the digital era, XML data is fundamental for various applications, requiring robust methods to ensure data integrity and security. Traditional digital watermarking techniques face challenges due to XML's hierarchical structure. Zero-watermarking, which derives a watermark from inherent data properties without altering the data, offers a promising alternative. This paper enhances zero-watermarking robustness for XML by integrating usability queries to filter functional dependencies (FDs) based on their relevance. This approach maintains watermark integrity even when some data elements are modified, provided the data's usability is preserved. The proposed method increases resilience against targeted attacks altering semantic information while preserving usability. Key contributions include incorporating usability queries into the FD-based zero-watermarking process, generating cover ranges for usability queries, and empirically testing the method's robustness.

Data security has become more important over the last few years as data sharing over the world has become trivial. Data ownership therefore becomes critical as data can be very valuable and vulnerable to theft. Watermarking is a technique that can help data owners prove ownership over their data. In this paper, an approach is presented to watermark data that is gathered over time, such as weather data. With this research, we propose an adaptation of an existing audio watermarking technique developed in [1]. The adapted algorithm embeds a bit stream into a non-medical time series dataset by calculating the Discrete Wavelet Transform coefficients and modifying their magnitudes. The algorithm shows good robustness against a small range of data modification attacks but lacks capability in larger
scaled attacks. In addition, the proposed algorithm does require additional research to use it in a professional setting.

The advancement of Machine Learning (ML) in the last decade has created new business prospects for developers working on ML models. Models that are expensive and time-consuming to design and train can now be outsourced from others to reduce costs using Machine Learning as a service (MLaaS). \textbf{Deep Neural Networks (DNNs)} are particularly expensive to train, therefore many who need a DNN utilize the services of an MLaaS provider. This creates the \textbf{possibility of piracy} of this valuable asset, and the need to prevent piracy to assure a fair market. To address this need, research has been conducted on protecting DNNs using various watermarking techniques. A work by \textit{Li et al.} has proposed null-embedding, a technique that renders the DNN useless if it is subject to a piracy attack. Despite being effective, this method was shown to reduce classification performance when embedding a watermark into the model. This paper suggests modifications to the null-embedding technique that reduce this impact and keep the classification accuracy close to that of a non-watermarked model.

AI and machine learning have been topics of big interest in the last couple of years, with plenty of applications in many domains. To train these models into useful and desirable tools, a large amount of data is necessary. This data is expensive to collect, becoming one of the most valuable commodities of this century. As the value of data increases, protecting this intellectual property becomes more and more relevant. Watermarking is a technique widely used for data protection in media, but the non-media counterpart has not been researched as thoroughly. In this paper, an adaptation of a common watermarking technique, DWT watermarking, is applied on two datasets used for machine learning. This technique is invisible and robust in signal watermarking, but its performance on a numerical dataset has not been previously researched. A previously devised algorithm was used, but it was adjusted to better fit dataset watermarking. To assess the quality of the watermark, the marked data has been subjected to create, remove, update and zero-out attacks. On top of this, multiple machine-learning models have been trained on the marked data. Initial results show that the proposed technique performs well in terms of invisibility, obtaining similar or better accuracies than models trained on the original data, but it is quite sensitive to attacks. Even small modifications, less than 1\% of the data, can break the signature.

Datasets play an important role in machine learning technology. The quality of a machine learning model is highly dependent on the quality of the training dataset. Datasets are of great economic value and should be viewed as intellectual property. To protect the property rights of machine learning training datasets, we can make use of the watermarking technique. In this paper, we propose a dataset watermarking method for numerical datasets. Our method is modified from the radioactive data method, which is proposed for image datasets. Our method can detect if a linear classifier machine learning model has been trained with the watermarked dataset. The experiment results show that we can detect the watermark with more than 99% confidence with only 1% of data being modified. The watermarking method is not robust against data normalization but is robust against column dropping when the dimension of the dataset is high.

The media watermarking technique domain has had the last 30 years to develop itself. The non-media side, however, is a way newer sub-domain. [1] The data-gathering process for machine learning algorithms is a tedious and time consuming task. This becomes worse as the scale of these algorithms increases. Thus, protecting the datasets against illegal use or sale and proving they are intellectual property is useful. In this paper, we answer the question: How can image watermarking techniques be applied to classification algorithm datasets, without degrading the dataset's quality? Algorithms that use the Singular Value Decomposition (SVD) of the data are often the basis of other matrix decomposition based Image watermarking techniques. Thus if an SVD based algorithm can be applied to a machine learning dataset then the other matrix decomposition based algorithms can also be applied. This implies that a large part of the much older media targeted watermarking techniques can be applied to the non-media datasets. In this paper we apply the watermarking technique described in [2] to a machine learning dataset. This watermark provides decent imperceptibility and robustness against update, zero-out and insertion attacks but it's held back by its lackluster robustness against deletion attacks. That said, we proved that when an image watermark is found that is impervious against deletion attacks, it can be applied to the machine learning datasets.

Although digital watermarking has been a well-researched topic for the past decades and has seen numerous implementations for relational databases, it still lacks research for non-relational schema-less databases. In this paper, we explore proposed techniques for non-relational database watermarking and introduce an improved technique for NoSQL database watermarking. The improved technique produces pseudo documents, embeds a watermark in them, and inserts them into the database. Then it modifies the relations between the pseudo and real documents to indicate which documents are covered by the watermark. Lastly, the pseudo documents act as a cover for the watermark embedding, proving the authenticity of the documents connected. We tested the technique against numerous modification and deletion attacks. The results show that the introduced technique does not improve the robustness of the watermark. Although the technique has the potential to produce a robust watermark, it still needs to be improved before being used in a commercial setting.