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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 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. [ABSTRACT FROM AUTHOR]

Digital images are easily corrupted by attacks during transmission and most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, we propose a robust image data hiding method based on multiple backups and pixel bit weight (PBW). Especially multiple backups of every pixel bit are pre-embedded into a cover image according to a reference matrix. Since different pixel bits have different weights, the most significant bits (MSBs) occupy more weights on the secret image than those of the least significant bits (LSBs). Accordingly, some backups of LSBs are substituted by the MSBs to increase the backups of MSBs so that the quality of the extracted secret image can be improved. Experimental results show that the proposed algorithm is robust to cropping and noise attacks for secret image. [ABSTRACT FROM AUTHOR]

Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance. [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]

Most data hiding methods have limitations in resisting cropping and noise attacks. Aiming at this problem, a robust data hiding with multiple backups and optimized reference matrix is proposed in this paper. Specifically, secret data is divided into a set of groups and multiple backups of each group data are generated according to the number of backups. The cover image is divided into several blocks. A reference matrix is constructed by four constraints to assist data hiding and data extraction. The proposed method aims to extract exactly at least one backup of each group data so that the correct backups can construct the secret data well if the stego-image is corrupted. Experimental results show that the proposed algorithm is robust to cropping and noise attacks. [ABSTRACT FROM AUTHOR]

Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L 2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve. [ABSTRACT FROM AUTHOR]

Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination. [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]

In this paper, a reversible data hiding method in encrypted image (RDHEI) is proposed. Prior to image encryption, the embeddable pixels are selected from an original image according to prediction errors due to adjacent pixels with strong correlation. Then the embeddable pixels and the other pixels are both rearranged and encrypted to generate an encrypted image. Secret bits are directly embedded into the multiple MSBs (most significant bit) of the embeddable pixels in the encrypted image to generate a marked encrypted image during the encoding phase. In the decoding phase, secret bits can be extracted from the multiple MSBs of the embeddable pixels in the marked encrypted image. Moreover, the original embeddable pixels are restored losslessly by using correlation of the adjacent pixels. Thus, a reconstructed image with high visual quality can be obtained only when the encryption key is available. Since exploiting multiple MSBs of the embeddable pixels, the proposed method can obtain a very large embedding capacity. Experimental results show that the proposed method is able to achieve an average embedding rate as large as 1.7215 bpp (bits per pixel) for the BOW-2 database. [ABSTRACT FROM AUTHOR]

In this letter, a reversible data hiding (RDH) scheme by using adaptive pixel value prediction and adaptive embedding bin selection based on pixel-based pixel value ordering (PPVO) is proposed. Different from the previous PPVO based methods, each to-be-embedded pixel is predicted by its neighbor pixels, which are selected adaptively according to the complexity of its neighbor pixel values. Moreover, the value range of embedding bin is not constrained in our method. Especially, when the value of embedding bin is less than 0, its value is updated adaptively during the process of embedding and extracting secret bits. Our maximum embedding capacity (EC) is improved significantly due to unconstrained embedding bin. In addition, the optimal embedding bins can be selected to achieve highest visual quality under a given EC. Experimental results show that the proposed method outperforms some state-of-the-art PPVO based RDH methods. [ABSTRACT FROM AUTHOR]

Image encryption is a useful technique of image content protection. In this paper, we propose a novel image encryption algorithm by jointly exploiting random overlapping block partition, double spiral scans, Henon chaotic map, and Lü chaotic map. Specifically, the input image is first divided into overlapping blocks and pixels of every block are scrambled via double spiral scans. During spiral scans, the start-point is randomly selected under the control of Henon chaotic map. Next, image content based secret keys are generated and used to control the Lü chaotic map for calculating a secret matrix with the same size of input image. Finally, the encrypted image is obtained by calculating XOR operation between the corresponding elements of the scrambled image and the secret matrix. Experimental result shows that the proposed algorithm has good encrypted results and outperforms some popular encryption algorithms. [ABSTRACT FROM AUTHOR]

We propose a novel perceptual image hashing based on weighted discrete wavelet transform (DWT) statistical features. This hashing converts input image into a normalized image by bi-linear interpolation and color space conversion, extracts edge image of the normalized image via Canny operator, and divides the edge image into non-overlapping blocks. For each block, a three-level 2D DWT is applied to obtain different sub-bands and the weighted sum of the DWT statistics of these sub-bands is calculated. Finally, image hash is generated by concatenating and quantizing these weighted DWT features. Similarity of image hashes is measured by Euclidean distance. The Copydays dataset and the Uncompressed Color Image Database (UCID) are both used to evaluate classification between robustness and discrimination. Receiver operating characteristics curve comparisons illustrate that our hashing is superior to some state-of-the-art algorithms in classification performance with respect to robustness and discrimination. The LIVE Image Quality Assessment Database is used to validate our application in reduced-reference image quality assessment. Experimental results show that our hashing has better performance in image quality assessment than two popular measures, i.e. peak signal-to-noise ratio and structural similarity. [ABSTRACT FROM AUTHOR]

We propose a novel image encryption scheme that utilizes chaotic maps and random scan patterns to protect image content. Our scheme includes three stages: pixel permutation via random scan patterns, block pixel diffusion, and block permutation. In the first stage, the input image is partitioned into overlapping blocks, and random scan patterns controlled by chaotic maps are exploited to scramble pixels of blocks. In the second stage, content-based keys are generated from the input image, and the content-based keys and chaotic maps are used to calculate secure matrices for achieving diffusion. The block pixel diffusion is conducted by performing element-wise XOR operation between blocks and their corresponding secure matrices. In the third stage, the image is partitioned into non-overlapping blocks, and all blocks are randomly permuted to further improve security. Key space analysis shows that the key space of the proposed scheme is bigger than those of some existing schemes. Experiments on standard test images are conducted by using some well-known metrics, such as histogram, χ2 test, correlation coefficient, entropy, the number of pixels change rate, the unified average changing intensity, and peak signal-to-noise ratio. The results demonstrate that the proposed scheme reaches the state-of-the-art performance in security and speed. [ABSTRACT FROM AUTHOR]

A reversible data hiding (RDH) method with prediction error histogram (PEH) decomposition and hybrid dimensional histogram modification is proposed. First, a global one-dimensional (1D) PEH is generated by pixel-based pixel value ordering predictor. The proposed method fully utilizes the pixels inside the range of the minimum and maximum of neighbors to decompose 1D-PEH into several sub 1D-PEHs. Then, each sub 1D-PEH is separated into two parts. The part with small-magnitude is used to construct a reduced two-dimensional (2D) PEH, whereas the rest is shifted for reversibility. Finally, data embedding is performed by modifying each reduced 2D-PEH to reduce distortion. Experimental results demonstrates that the proposed method outperforms some high-fidelity RDH methods. [ABSTRACT FROM AUTHOR]

Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality. [ABSTRACT FROM AUTHOR]

We investigate the use of parabolic interpolation in data hiding and propose a novel data hiding algorithm with high capacity based on interpolated image. Specifically, the proposed algorithm creates an interpolated image from input image by parabolic interpolation, and embeds secret bits into interpolated pixels in terms of the relation between the interpolated value and the mean value. Ten standard benchmark images are taken as test images for validating efficiency of our algorithm. The results illustrate that our algorithm has better performances than some popular data hiding methods in embedding capacity and visual quality with respect to PSNR and SSIM. [ABSTRACT FROM AUTHOR]

In this paper, we propose a separable reversible data hiding method in encrypted image (RDHEI) based on two-dimensional permutation and exploiting modification direction (EMD). The content owner uses two-dimensional permutation to encrypt original image through encryption key, which provides confidentiality for the original image. Then the data hider divides the encrypted image into a series of non-overlapping blocks and constructs histogram of adjacent encrypted pixel errors. Secret bits are embedded into a series of peak points of the histogram through EMD. Direct decryption, data extraction and image recovery can be performed separately by the receiver according to the availability of encryption key and data-hiding key. Different from some state-of-the-art RDHEI methods, visual quality of the directly decrypted image can be further improved by the receiver holding the encryption key. Experimental results demonstrate that the proposed method outperforms some state-of-the-art methods in embedding capacity and visual quality. [ABSTRACT FROM AUTHOR]

CpG ODN acts as a 'pathogen-associated' molecular pattern that is recognized by intracellular Toll-like receptor 9 and can induce a robust dendritic cells (DCs) activation to against various diseases. However, the CpG ODN is restricted with critical defects of easily enzymolysis and negligible phagocytosis. To overcome these issues, a simpler and competent nanocarrier of mannose modified PEGylated branched PEI 25k (PEI-PEG-Man) was designed to achieve excellent DCsspecific delivery of CpG. Nanoparticles of PEI-PEG-Man encapsulating CpG (PEI-PEG-Man@CpG) possessed elevated gene loading capacity, biological stability and admirable anti-enzymolysis ability. PEI-PEG-Man@CpG could be selectively uptake by DCs through a receptor-mediated endocytosis, which generates a potent immunostimulatory activity on bone marrow derived dendritic cells (BMDCs) evidenced by significantly upregulation of the pro and anti-inflammatory cytokines (TNF- α , IL-6) and the co-stimulatory molecules (CD40, CD80, CD86, and MHC class II) on BMDCs in vitro . More importantly, the results of in vivo targeting assay showed that PEI-PEG-Man@CpG nanoparticles could remarkably boost CpG accumulation in lymph lodes upon subcutaneous administration in C57BL/6 mice, which facilitated maturation of DCs and productions of anti-inflammatory cytokines. Our results suggested that PEI-PEG-Man@CpG nanoparticles, in the future, might function as a powerful vector for ex vivo engineering to promote DC targeting and maturation, which enhance vaccine efficiency against cancer or infectious disease.