Dataset Watermarking Using the Discrete Wavelet Transform
Mike P. Raave (Student TU Delft)
Devri İş Ler (Carlos III University of Madrid, IMDEA Networks Institute)
Zekeriya Erkin (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
In this work, we focus on watermarking time series datasets and explore one of the techniques known from audio-watermarking, namely Discrete Wavelet Transform (DWT) based watermarking, to investigate its effectiveness. We adapt (Attari and A. Shirazi, 2018) and embed a bit stream into a time series dataset by calculating the DWT coefficients and modifying their magnitudes for embedding. Our experimental results on two real-world datasets show good robustness against a small range of data modification attacks but lack capability in larger-scale attacks. We believe that our work could initiate a new research direction on dataset watermarking using well-known techniques from signal processing.
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