Progressive secret image sharing (PSS) can progressively recover different resolution images or different parts of an image. To protect image privacy on the cloud and IoT application scenarios, this paper first presents an efficient PSS scheme through the integration of two deep learning networks and reversible data hiding in encrypted images. After sharing different parts of the original image, the receiver can progressively recover: a low-resolution image, a mask image where the most sensitive area is peeled off, and the original image according to the number of holding shares and image encryption keys. Some additional data such as signatures, fingerprints, and timestamps can be also embedded into each share on the cloud server, as the data extraction is separate from image recovery. Experimental results demonstrate that the proposed scheme can uniquely and efficiently obtain a recovered image progressively. Our scheme also has a higher security level, and the average embedding rate achieves almost a 1 bpp improvement compared to state-of-the-art schemes on a larger-scale image dataset.

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.

Nowadays, we greatly depend on Internet mass transmission, of all kinds of data, including critical information among them. Therefore, secure communication is an important topic. Data hiding can embed critical information into carriers, such as images, videos, and so on. Efficiently embedding information into images is the goal of this research. Image steganography techniques utilise a cover image to hide secret data and produce a stego-image by modifying pixel values. After modification, the stego-image is distorted with respect to the cover image. Vector quantisation (VQ) is a lossy image compression technique. The image recovered from the VQ compressed code has distortion compared to the original image. Based on the VQ image, we can hide secret data by modifying the pixel value in a way that the distortion is compensated. The embedding rate of the proposed scheme is adjustable. Experimental results show that our scheme can achieve a high embedding rate in comparison with related works. For low-quality VQ images, embedding can improve the visual quality of the stego-image at the same time.