In recent years, reversible data hiding in encrypted domain (RDH-ED) has garnered considerable interest among researchers, resulting in the development of high-performance methods based on various carriers. However, the challenge of enhancing the data embedding capacity while ensuring reversibility becomes increasingly pronounced when the carrier is a three-dimensional (3D) model. In this paper, a high capacity RDH-ED method based on dynamic prediction and virtual connection for 3D models is proposed. Unlike existing methods that partition the vertices in the model into embeddable and prediction sets, where each vertex can only serve one function, the proposed dynamic prediction mechanism constructs a data embedding order set by leveraging the connectivity relationships between vertices. This allows each vertex within the set to both embed data and provide predictions, significantly increasing the proportion of embeddable vertices. Moreover, the proposed method is the first work to consider independent vertices within the model and integrates a novel virtual connection approach with the dynamic prediction process, enabling all independent vertices to participate in data embedding and prediction, thereby further enhancing the data embedding capacity. Experimental results demonstrated that the proposed method significantly outperforms other state-of-the-art methods in terms of data embedding capacity while ensuring reversibility.

Glyph perturbation adjusts the characters’ structures and strokes to make the original characters change subtly, which cannot be detected by the naked eye. These generated variants with different glyph perturbation can represent different status of secret messages, which can be used to embed information in Chinese text documents. However, Chinese characters have characteristics in large numbers, complex structures, and diverse fonts, which limit the generation of glyph perturbation and make the design of Chinese characters time-consuming and laborious. Many font style transfer methods for Chinese characters have been proposed to improve the efficiency of Chinese character generation based on deep learning. At present, there are few studies on efficient font style transfer for glyph perturbation of Chinese characters. In this paper, a stylized glyph perturbation method based on style extractor and attention augmented convolution is proposed. It adopts a multi-head attention mechanism to enhance convolution in the font transfer, which concatenates the convolution feature maps and the self-attention activation maps to weaken the limitations of ordinary convolution in processing images. The extracted style features are sent into the decoder of the font transfer network so as to improve the stylized ability. Particularly, the impact of style extractor and attention augmented convolution on the glyph perturbation generation is addressed. The extraction accuracy and embedding capacity are tested in our experiments. The embedding capacity of secret message can achieve around 1.8 bit/character.

Recently, reversible data hiding in encrypted images (RDHEI) has received widespread attention from researchers. To embed high payload into encrypted images while maintaining sufficient security, a novel RDHEI algorithm in combination with consecutive zero-valued high bit-planes compression, bit-plane swapping as well as block rearrangement is proposed in this article. The proposed method is the first work to compress global zero-valued high bit-planes in a block-wise manner and adaptively allocate different Huffman indicators based on the occurrence frequency of zero-valued bit-planes so that a higher embedded payload is greatly provided. Unlike existing RDHEI methods embedded with unencrypted auxiliary information, resulting in low security, the bit-plane swapping and block rearrangement are subtly designed to cluster together all embeddable bit-planes, which enables most auxiliary information to be encrypted, largely enhancing the security and facilitating data embedding and data extraction. The experiment results demonstrate that the proposed method outperforms some state-of-the-art RDHEI methods in terms of security and payload. The average payload of the proposed method for two publicly-used datasets including BOSSbase and BOWS-2, are 3.793 bpp and 3.705 bpp, respectively.