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Hashing scheme is a high‐efficiency technique for processing massive images. Two critical metrics of the hashing scheme are discrimination and robustness, but most schemes do not get satisfied classification performance between them. This paper proposes a novel hashing scheme via image feature map and 2D PCA. First, the proposed scheme extracts local phase quantization (LPQ) features in the frequency domain and local ternary pattern (LTP) features in the spatial domain, and combines them to construct an image feature map. Second, the proposed scheme conducts dimension reduction via 2D PCA for learning features from the image feature map. Last, the learned features are compressed to generate the hash sequence. Performances are tested on open image datasets. The results demonstrate that the proposed scheme can make a good balance between discrimination and robustness. In addition, the classification and copy detection of the proposed scheme are both superior to those of some famous hashing schemes. [ABSTRACT FROM AUTHOR]

Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block‐based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS‐2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state‐of‐the‐art RDH algorithms in embedding capacity. [ABSTRACT FROM AUTHOR]

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub‐band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination. [ABSTRACT FROM AUTHOR]