Abstract: To address the copyright protection issue in the field of image classification datasets, a traceable method based on invisible backdoor watermarking, named IBWOD, is proposed. This method ensures the watermark’s strong concealment while maintaining good usability and effectiveness. Firstly, an encoder-decoder network is used to embed the backdoor watermark into selected samples, generating watermark samples. Secondly, the labels of these watermark samples are modified to specified labels, and then the watermark samples are merged with unmodified samples to form a watermark dataset. Models trained using this watermark dataset will leave a specific backdoor, i.e., a mapping relationship from the backdoor watermark to the specified labels. Finally, a corresponding model verification algorithm is proposed, based on this special mapping relationship, to verify if a suspicious model has used the watermark dataset. Experimental results demonstrate that IBWOD can effectively verify whether a model has used the watermark dataset and possesses strong concealment. 

Key words: open-sourced dataset, copyright protection, backdoor watermarking, machine learning, image classification