Reversible data hiding in encrypted images (RDHEI) is an effective technique of data security. Most state-of-the-art RDHEI methods do not achieve desirable payload yet. To address this problem, we propose a new RDHEI method with hierarchical embedding. Our contributions are twofold. (1) A novel technique of hierarchical label map generation is proposed for the bit-planes of plaintext image. The hierarchical label map is calculated by using prediction technique, and it is compressed and embedded into the encrypted image. (2) Hierarchical embedding is designed to achieve a high embedding payload. This embedding technique hierarchically divides prediction errors into three kinds: small-magnitude, medium-magnitude, and large-magnitude, which are marked by different labels. Different from the conventional techniques, pixels with small-magnitude/large-magnitude prediction errors are both used to accommodate secret bits in the hierarchical embedding technique, and therefore contribute a high embedding payload. Experiments on two standard datasets are discussed to validate the proposed RDHEI method. The results demonstrate that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average payloads of the proposed RDHEI method are 3.4568 bpp and 3.6823 bpp for BOWS-2 dataset and BOSSbase dataset, respectively.

This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of our TD hashing. A popular TD algorithm, called Tucker decomposition, is then exploited to decompose the three-order tensor into a core tensor and three orthogonal factor matrices. As the factor matrices can reflect intrinsic structure of original tensor, hash construction with the factor matrices makes a desirable discrimination of the TD hashing. To examine these claims, there are 14,551 images selected for our experiments. A receiver operating characteristics (ROC) graph is used to conduct theoretical analysis and the ROC comparisons illustrate that the TD hashing outperforms some state-of-the-art algorithms in classification performance between the robustness and discrimination.

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.

Image captioning is a challenging task, i.e., given an image machine automatically generates natural language that matches its semantic content and has attracted much attention in recent years. However, most existing models are designed manually, and their performance depends heavily on the expert experience of the designer. In addition, the computational flow of the model is predefined, and hard and easy samples will share the same coding path and easily interfere with each other, thus confusing the learning of the model. In this paper, we propose a Dynamic Transformer to change the encoding procedure from sequential to adaptive, i.e., data-dependent computing paths. Specifically, we design three different types of visual feature extraction blocks and deploy them in parallel at each layer to construct a multi-layer routing space in a fully connected manner. Each block contains a calculation unit that performs the corresponding operations and a routing gate that learns to adaptively select the direction to pass the signal based on the input image. Thus, our model can achieve a robust visual representation by exploring potential visual feature extraction paths. We evaluate our method quantitatively and qualitatively using a benchmark MSCOCO image caption dataset and perform extensive ablation studies to investigate the reasons behind its effectiveness. The experimental results show that our method is significantly superior to previous state-of-the-art methods.