Robust reversible watermarking (RRW) is one of the most popular areas in information hiding. Existing schemes have two drawbacks: 1) schemes that can resist conventional attacks often fail to resist geometric attacks, and 2) schemes that can resist geometric attacks often are not robust against conventional attacks and have poor stability. Inspired by the high robustness of fractional-order orthogonal moments (FoOM) and the good feature of resistance to geometric attacks of Zernike moments and pseudo-Zernike moments (ZM/PZM), in this research, FoOM is used to optimize ZM/PZM to obtain FoZM/FoPZM (namely, fractional-order Zernike moments and fractional-order pseudo-Zernike moments). Furthermore, a denoiser is proposed to preprocess the watermarks to improve the robustness against geometric and conventional attacks, the amount of extracted auxiliary information is decreases and the extraction process of auxiliary information is designed to be more stable. Specifically, first, the source of the difference between the watermarked image and the carrier image is identified, that difference is represented with less information, and then that information is embedded into the cover image as auxiliary information. Second, the watermark is embedded in the low-order FoZM/FoPZM component. Finally, the watermarked image is denoised using a denoiser before extracting the watermark. The experimental results show that the scheme has good stability and strong robustness. Compared with existing methods, the proposed scheme has a small and stable auxiliary information size, strong robustness to noise attacks such as Gaussian noise and salt-and-pepper noise attacks, and better resistance to geometric attacks such as rotation and scaling attacks.

Deep neural networks are widely used for voiceprint recognition, whilst voiceprint recognition models are vulnerable to attacks. Existing protection schemes for voiceprint recognition models are insufficient to withstand various robustness attacks and cannot prevent model theft. This paper proposes a black-box voiceprint recognition model protection framework that combines active and passive protection. It embeds key information into the Mel spectrogram to generate trigger samples that are difficult to detect and remove and injects them into the host model as watermark W, thereby enhancing the copyright protection performance of the voiceprint recognition model. To restrict the use of the model by unauthorized users, the index number corresponding to the model and the encrypted model information are stored on the blockchain, and then, an exclusive smart contract is designed to restrict access to the model. Experimental results show that this framework effectively protects voiceprint recognition model copyrights and restricts unauthorized access.