arXiv:2403.05628v1 Announce Type: new
Abstract: Curating high quality datasets that play a key role in the emergence of new AI applications requires considerable time, money, and computational resources. So, effective ownership protection of datasets is becoming critical. Recently, to protect the ownership of an image dataset, imperceptible watermarking techniques are used to store ownership information (i.e., watermark) into the individual image samples. Embedding the entire watermark into all samples leads to significant redundancy in the embedded information which damages the watermarked dataset quality and extraction accuracy. In this paper, a multi-segment encoding-decoding method for dataset watermarking (called AMUSE) is proposed to adaptively map the original watermark into a set of shorter sub-messages and vice versa. Our message encoder is an adaptive method that adjusts the length of the sub-messages according to the protection requirements for the target dataset. Existing image watermarking methods are then employed to embed the sub-messages into the original images in the dataset and also to extract them from the watermarked images. Our decoder is then used to reconstruct the original message from the extracted sub-messages. The proposed encoder and decoder are plug-and-play modules that can easily be added to any watermarking method. To this end, extensive experiments are preformed with multiple watermarking solutions which show that applying AMUSE improves the overall message extraction accuracy upto 28% for the same given dataset quality. Furthermore, the image dataset quality is enhanced by a PSNR of $approx$2 dB on average, while improving the extraction accuracy for one of the tested image watermarking methods.
Curating high quality datasets and ownership protection
Curating high quality datasets is a crucial aspect in the development of new AI applications. However, creating such datasets requires significant time, money, and computational resources. As a result, effective ownership protection of these datasets is becoming increasingly important.
Dataset watermarking for ownership protection
To protect the ownership of image datasets, imperceptible watermarking techniques have been employed. These techniques involve embedding ownership information, or watermarks, into individual image samples. However, embedding the entire watermark into all samples can lead to redundancy, which can negatively impact the quality of the dataset and the accuracy of watermark extraction.
The AMUSE method: Multi-segment encoding-decoding for dataset watermarking
In this paper, the authors propose a new method called Adaptive Multi-Segment Encoding-Decoding (AMUSE) for dataset watermarking. This method aims to address the issues of redundancy and extraction accuracy by adaptively mapping the original watermark into a set of shorter sub-messages and vice versa.
Adaptive message encoding
The message encoder in the AMUSE method is adaptive, meaning it adjusts the length of the sub-messages based on the protection requirements for the target dataset. This ensures that the watermark is embedded in a way that minimizes redundancy and maintains the desired level of protection.
Utilizing existing watermarking methods
The AMUSE method utilizes existing image watermarking methods to embed the sub-messages into the original images in the dataset and extract them from the watermarked images. This plug-and-play approach allows the encoder and decoder to be easily integrated into any watermarking method.
Experiments and results
The proposed AMUSE method was tested against multiple watermarking solutions in extensive experiments. The results showed that applying AMUSE improved the overall message extraction accuracy by up to 28% for the same dataset quality. Additionally, the image dataset quality was enhanced by an average Peak Signal-to-Noise Ratio (PSNR) improvement of approximately 2 dB. These improvements were achieved while also enhancing the extraction accuracy for one of the tested image watermarking methods.
Relation to multimedia information systems and AR/VR
The concept of dataset watermarking presented in this paper is highly relevant to the wider field of multimedia information systems. Multimedia information systems involve the storage, retrieval, and manipulation of various forms of media, including images, videos, and audio. Protecting the ownership and integrity of these media is crucial in applications such as content distribution, copyright protection, and digital forensics.
Moreover, as augmented reality (AR), virtual reality (VR), and artificial reality continue to advance, the need for authentic and trustworthy multimedia content becomes even more important. Dataset watermarking techniques, such as the AMUSE method, play a vital role in ensuring the integrity of the digital assets used in AR/VR experiences and applications.
By protecting the ownership of datasets and improving extraction accuracy without compromising dataset quality, the AMUSE method contributes to the broader field of multimedia information systems and helps lay the foundation for more reliable and secure AI applications, AR/VR experiences, and digital content distribution.