Multipurpose Watermarking Approach for Copyright and Integrity of Steganographic Autoencoder Models

With the great achievements of deep learning technology, neural network models have emerged as a new type of intellectual property. Neural network models’ design and training require considerable computational resources and time. Watermarking is a potential solution for achieving copyright protection and integrity of neural network models without excessively compromising the models’ accuracy and stability. In this work, we develop a multipurpose watermarking method for securing the copyright and integrity of a steganographic autoencoder referred to as “HiDDen.” This autoencoder model is used to hide different kinds of watermark messages in digital images. Copyright information is embedded with imperceptibly modified model parameters, and integrity is verified by embedding the Hash value generated from the model parameters. Experimental results show that the proposed multipurpose watermarking method can reliably identify copyright ownership and localize tampered parts of the model parameters. Furthermore, the accuracy and robustness of the autoencoder model are perfectly preserved.

Multipurpose Watermarking Algorithm for Medical Images

Considering the existing medical image watermarking algorithms, a single function often has limitations, and a multipurpose watermarking algorithm for medical images is proposed. First, medical images are divided into regions of interest (ROIs) and regions of noninterest (RONIs). Then, the authentication watermark produced for each subblock of the ROI is embedded into the corresponding mapping subblock. The visible watermark is embedded into the RONI, and, finally, the watermark information and constructed authentication information in each subblock of the ROI are embedded into the corresponding RONI subblock. Simulation results show that the embedded visible watermark can protect and facilitate medical image management. In addition, the proposed algorithm has strong robustness and very good visual quality. It can simultaneously realize copyright protection and content authentication and also has high tamper localization capability.

Multipurpose Watermarking Based on SWT and SVD

Dual digital watermarking has emerged as a successful solution for copyright protection, tamper detection and localization. However, several problems related to the robustness, capacity, tampered area detection still mystifying. This paper presents a high capacity dual watermarking mechanism for digital color image. An invisible robust watermark is embedded in the Green component of the host image by using a hybrid combination of Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD) for copyright protection. A fragile invisible watermark based on the Least Significant Bit (LSB) replacement approach is embedded in the Blue component of the image for tamper detection and localization. The proposed technique focuses on robustness and imperceptibility while maximizing embedding capacity that makes this technique a multipurpose watermarking scheme.