AMUSE: Adaptive Multi-Segment Encoding for Dataset Watermarking

Saeed Ranjbar Alvar, Mohammad Akbari, David Ming Xuan Yue, Yong Zhang

Abstract: Curating high-quality datasets requires significant resources, making ownership protection crucial. Recently, to protect the ownership of an image dataset, imperceptible watermarking is used to store ownership information (i.e., watermark) into the individual samples. However, embedding the entire watermark into all samples causes redundancy, damaging 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. The message encoder adjusts sub-message lengths based on protection requirements, and the decoder reconstructs the original message from the extracted sub-messages. AMUSE is plug-and-play and orthogonal to existing image watermarking methods. Extensive experiments with multiple watermarking solutions demonstrate that AMUSE improves the dataset quality and message extraction accuracy. The code is available in https://github.com/vbdi/amuse.

External IDs:dblp:conf/icmcs/AlvarAYZ25