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Reversible data hiding in encrypted images (RDHEI) is an essential data security technique. Most RDHEI methods cannot perform well in embedding capacity and security. To address this issue, we propose a new RDHEI method using Chinese remainder theorem-based secret sharing (CRTSS) and hybrid coding. Specifically, a hybrid coding is first proposed for RDH to achieve high embedding capacity. At the content owner side, a novel iterative encryption is designed to conduct block based encryption for perfectly preserving the spatial correlation of original blocks in their encrypted blocks. Then, the CRTSS with the constraints is exploited to generate multiple encrypted image shares, in which spatial correlations of the encrypted blocks are also preserved. Meanwhile, the CRTSS provides good security properties for the proposed method. Since there are strong spatial correlations in the blocks of each share, the data-hider can exploit the proposed hybrid coding to perform data embedding for improving capacity. On the receiver side, even if some shares are corrupted/missing, the original image can be losslessly recovered as long as enough uncorrupted marked shares are obtained. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art methods, including some secret sharing (SS) based methods in terms of embedding capacity.

Digital images are easily corrupted during transmission. Most image denoising methods cannot perform well on restoring the secret image extracted from a corrupted stego image. To deal with this issue, we propose a new secret image restoration method with convex hull and elite opposition-based learning strategy. Specifically, the pixel distortion values of the corrupted secret image are calculated and used to classify the pixels into trustable pixels or untrusted pixels. For an untrusted pixel, a convex hull is generated by its context trustable pixels due to the irregular distribution of trustable pixels. The untrusted pixel in the convex hull is restored by the trustable pixels within the convex hull. The other untrusted pixels are restored using elite opposition-based learning strategy. The experimental results show that the proposed method outperforms some state-of-the-art methods regarding recovered secret image quality.