Robustness is the most important factor that limits the practical application of reversible watermarking. To deal with this issue, several robust reversible watermarking (RRW) techniques have been proposed. Among them, the twostage RRW framework proposed by Coltuc et al. is a promising one. In the first state of this framework, a robust watermark is embedded into the cover image to provide robustness, and then in the second stage, the information enabling revert the robust embedding is reversibly embedded into the already marked image to guarantee the reversibility. However, because of using the same area for these two embedding stages, the robustness in the first stage is seriously weakened by the reversible embedding. As a result, this elegant method is not effective as expected. Based on this consideration, this paper proposes an independent embedding domain (ED) based two-stage RRW. The cover image is first transformed into two independent EDs, and then the robust and reversible watermarks are embedded into each domain separately. The carrier derived from the first embedding stage that carrying the robust watermark will not change after the reversible embedding, and thus the robustness of the first stage is well preserved. By the proposed method, the embedding performance of the original two-stage RRW is significantly enhanced. Moreover, the proposed method is experimentally verified better than some other state-of-the-art RRW methods.

The face recognition has become a common means of identity authentication because of the advantages of uniqueness, non-invasive and not easy to be stolen. The outsourcing of face recognition to the service provider is a typical manner nowadays. However, it raises important concerns about the privacy of outsourcing server due to the sensitivity of face data. Therefore, a frame of identity authentication based on technology of privacy-preserving face recognition is presented in this paper. Convolutional neural network (CNN) is used for face feature extraction. To overcome the issue of privacy leaked, a method of secure nearest neighbor that can compute the cosine similarity over encrypted feature vectors is proposed. What’s more, edge computing is introduced in our frame to increase the authentication efficiency by removing some operations from cloud to the edge of Internet. Moreover, we also propose a secret sharing homomorphism technology which is used for distributed computing to improve the fault-tolerance of our identity authentication system. Experimental results show that the proposed schemes are secure and effective.

With the transmission and sharing of data in the Internet of Things (IoT), while bringing development to life and the economy, it also inevitably threatens the data copyright protection and authentication. Digital watermarking technology can provide an effective solution for copyright protection by embedding the watermark in the data to prove the copyright attribution. The existing methods of digital watermarking in IoT mainly target multimedia, without considering the copyright authentication in database data. Unlike multimedia information, the database does not focus on the subjective visual perception when using the data, but rather on the potential values unlocked from the data through algorithms such as data mining. Therefore, we propose a new database watermarking algorithm based on decision tree shift correction (DTSC), considering the data copyright authentication and usability when applying for data mining algorithm. The algorithm adjusts the watermarked data by the DTSC method and makes the watermarked decision tree identical to the original in the iteration process. It solves the problem of database data copyright authentication in IoT and ensures the usability of the data when used for decision tree model construction. From the simulation results, it can be seen that the proposed method ensures the usability of the data for the classification and regression tree decision tree algorithm while embedding the watermark in the database data, and the data distortion of the proposed method does not differ from that of the traditional watermarking algorithm.

Aiming at the problem that the robustness, visibility, and transparency of the existing visible watermarking technologies are difficult to achieve a balance, this paper proposes an adaptive embedding method for visible watermarking. Firstly, the salient region of the host image is detected based on superpixel detection. Secondly, the flat region with relatively low complexity is selected as the embedding region in the nonsalient region of the host image. Then, the watermarking strength is adaptively calculated by considering the gray distribution and image texture complexity of the embedding region. Finally, the visible watermark image is adaptively embedded into the host image with slight adjustment by just noticeable difference (JND) coefficient. The experimental results show that our proposed method improves the robustness of visible watermarking technology and greatly reduces the risk of malicious removal of visible watermark image. Meanwhile, a good balance between the visibility and transparency of the visible watermark image is achieved, which has the advantages of high security and ideal visual effect.