With the proliferation of cloud applications, users are increasingly uploading their private images to cloud servers to avail of supplement storage capacity. Unlike image encryption, the thumbnail-preserving technique is a way of safeguarding image privacy and preserving the outline of the thumbnail image, which allows authorized users to recognize it according to prior knowledge of the original image. Recently, several thumbnail-preserving encryption schemes have been proposed. But most of the sum-preserving encryption-based schemes are irreversible, thereby significantly constraining their practical value. Moreover, the visual quality of images generated with the reversible thumbnail-preserving schemes is unsatisfactory, leading to a decrease in the user recognition accuracy. In light of the aforementioned, this paper presents an efficient scheme to generate thumbnail-preserving images with high visual naturalness, while ensuring reversibility of the process. Experimental results demonstrate that the proposed scheme achieves significant visual quality improvements compared to state-of-the-art schemes. Furthermore, the thumbnail-preserving images generated by using our scheme can effectively evade detection by confusing malicious attackers.

Deep learning is regarded as a promising solution for reversible steganography. There is an accelerating trend of representing a reversible steo-system by monolithic neural networks, which bypass intermediate operations in traditional pipelines of reversible steganography. This end-to-end paradigm, however, suffers from imperfect reversibility. By contrast, the modular paradigm that incorporates neural networks into modules of traditional pipelines can stably guarantee reversibility with mathematical explainability. Prediction-error modulation is a well-established reversible steganography pipeline for digital images. It consists of a predictive analytics module and a reversible coding module. Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility. The objective of this study is to evaluate the impacts of different training configurations upon predictive accuracy of neural networks and provide practical insights. In particular, we investigate how different initialisation strategies for input images may affect the learning process and how different training strategies for dual-layer prediction respond to the problem of distributional shift. Furthermore, we compare steganographic performance of various model architectures with different loss functions.

With the rapid development of smartphones, cloud storage, and wireless communications, protecting the security of compressed images through data transmission on the Internet has become a critical contemporary issue. A series of data hiding methods for AMBTC compressed images has been proposed to solve this problem. However, most of these methods either change the file size of the final compressed code or exchange the order of two quantization values in some blocks. To reverse this situation, this paper proposes a data hiding method for strict AMBTC format images using a hybrid strategy: replacement, matrix encoding, and symmetric quantization value embedding for three block types i.e., smooth blocks, less complex blocks and highly complex blocks. According to the hybrid strategy, an efficient data hiding order is designed to achieve higher-fidelity. Experimental results show that our proposed method provides an excellent balance between image quality and hiding capacity and has no error blocks in the final stego-compressed code.

The popularity of cloud computing has impelled more users to upload personal data into the cloud server. The need for secure transmission and privacy protection has become a new challenge and has attracted considerable attention. In this paper, we propose a high-capacity reversible data hiding scheme in encrypted images (RDHEI) that compresses prediction errors in usable blocks of block-based encrypted images. On the content owner side, the original image is divided into sized blocks, and each block is encrypted by block-based modulation. On the data hider side, an efficient block-based predictor is utilized to generate prediction errors. The Huffman coding technique is introduced to compress prediction errors in the usable blocks to embed abundant additional data. On the receiver side, the additional data can be totally extracted with a data hiding key and the original image can be losslessly recovered with an image encryption key. Experimental results demonstrate that the embedding rate of our proposed scheme is significantly improved compared to those of state-of-the-art schemes.