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Image hiding aims to hide the secret data in the cover image for secure transmission. Recently, with the development of deep learning, some deep learning-based image hiding methods were proposed. However, most of them do not achieve outstanding hiding performance yet. To address this issue, we propose a new image hiding framework called CAE-NF, which consists of compressive autoencoders (CAE) and normalizing flow (NF). Specifically, CAE's encoder respectively maps the secret image and cover image into the corresponding feature vectors. Image hiding and recovery can be modelled as the forward and backward processes of NF since NF is an invertible neural network. NF maps two feature vectors to a stego-image by its forward process. On the recovery side, the stego-images are mapped to two feature vectors by NF's backward process. Finally, the secret image is recovered by CAE's decoder. The proposed framework can achieve a good trade-off between the stego-image quality and recovered secret image quality, and meanwhile, improve the hiding and recovery performances. The experimental results demonstrate that the proposed framework significantly outperforms some state-of-the-art methods in terms of invisibility, security, and recovery accuracy on various datasets.

Image hashing is an effective technique for copy detection. This article proposes a robust color image hashing scheme with nonnegative matrix factorization (NMF) and saliency maps. The proposed hashing scheme constructs a weighted feature map via the saliency map and the matrix of color vector angles. It extracts hash from the weighted feature map by using 2-D discrete wavelet transform (DWT), NMF, and ordinal measurements. The use of 2-D DWT achieves an initial data reduction, the NMF provides a parts-based representation, and the ordinal measurements can make a short and robust sequence. These techniques jointly contribute to a robust, short, and discriminative hash. Experiments of classification and copy detection are both done. Comparative results indicate that the proposed hashing scheme outperforms the compared hashing schemes.

Robust hashing is a useful technique for the image applications of watermarking, authentication, quality assessment and copy detection. This article proposes a new robust hashing for image copy detection by using local tangent space alignment (LTSA). A key contribution is the weighted visual map computation based on the difference of Gaussian (DOG) and visual attention model. The weighted visual map can provide the proposed method with good robustness. Another contribution is the feature learning via LTSA from the feature matrix of the weighted visual map in discrete cosine transform domain. As it can maintain the local geometric relationships within image, the learned features can make the proposed method discriminative. Extensive experiments on public databases are conducted to validate the proposed robust hashing method. Compared with some famous robust hashing methods, the proposed robust hashing method demonstrates preferable classification performance in terms of discrimination and robustness. Copy detection performance is tested and the result verifies effectiveness of the proposed robust hashing method.

Multi-view clustering has gained great progress recently, which employs the representations from different views for improving the final performance. In this paper, we focus on the problem of multi-view clustering based on the Markov chain by considering low-rank constraints. Since most existing methods fail to simultaneously characterize the relations among different entries in a tensor from the global perspective and describe local structures of similarity matrices of a tensor, we propose a novel Flexible Tensor Learning for Multi-view Clustering with the Markov chain (FTLMCM) to solve this problem. We also construct transition probability matrices based on the Markov chain to fully utilize the connection between the Markov chain and spectral clustering. Specifically, the low-rank constraints of the tensor, the frontal slices and the lateral slices of the tensor are imposed on the objective function of the proposed method to achieve these goals. Besides, these three constraints can be optimized jointly to achieve mutual refinement. FTLMCM also uses the tensor rotation to better explore the relationships among different views. We formulate FTLMCM as a problem of low-rank tensor recovery and solve it with the augmented Lagrangian multiplier. Experiments on six different benchmark data sets under six metrics demonstrate that the proposed method is able to achieve better clustering performance.