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Zhenjun Tang
Guangxi Key Lab of Multi-Source Information Mining & Security, and Department of Computer Science, Guangxi Normal University, Guilin 541004, China

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Journal article
Published: 03 July 2021 in Journal of Visual Communication and Image Representation
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Video hashing is a useful technique of many multimedia systems, such as video copy detection, video authentication, tampering localization, video retrieval, and anti-privacy search. In this paper, we propose a novel video hashing with secondary frames and invariant moments. An important contribution is the secondary frame construction with 3D discrete wavelet transform, which can reach initial data compression and robustness against noise and compression. In addition, since invariant moments are robust and discriminative features, hash generation based on invariant moments extracted from secondary frames can ensure good classification of the proposed video hashing. Extensive experiments on 8300 videos are conducted to validate efficiency of the proposed video hashing. The results show that the proposed video hashing can resist many digital operations and has good discrimination. Performance comparisons with some state-of-the-art algorithms illustrate that the proposed video hashing outperforms the compared algorithms in classification in terms of receiver operating characteristic results.

ACS Style

Zhenjun Tang; Shaopeng Zhang; Xianquan Zhang; Zhixin Li; Zhenhai Chen; Chunqiang Yu. Video Hashing with Secondary Frames and Invariant Moments. Journal of Visual Communication and Image Representation 2021, 79, 103209 .

AMA Style

Zhenjun Tang, Shaopeng Zhang, Xianquan Zhang, Zhixin Li, Zhenhai Chen, Chunqiang Yu. Video Hashing with Secondary Frames and Invariant Moments. Journal of Visual Communication and Image Representation. 2021; 79 ():103209.

Chicago/Turabian Style

Zhenjun Tang; Shaopeng Zhang; Xianquan Zhang; Zhixin Li; Zhenhai Chen; Chunqiang Yu. 2021. "Video Hashing with Secondary Frames and Invariant Moments." Journal of Visual Communication and Image Representation 79, no. : 103209.

Original research paper
Published: 11 May 2021 in IET Image Processing
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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.

ACS Style

Zhenjun Tang; Mingyuan Pang; Chunqiang Yu; Guijin Fan; Xianquan Zhang. Reversible data hiding for encrypted image based on adaptive prediction error coding. IET Image Processing 2021, 15, 2643 -2655.

AMA Style

Zhenjun Tang, Mingyuan Pang, Chunqiang Yu, Guijin Fan, Xianquan Zhang. Reversible data hiding for encrypted image based on adaptive prediction error coding. IET Image Processing. 2021; 15 (11):2643-2655.

Chicago/Turabian Style

Zhenjun Tang; Mingyuan Pang; Chunqiang Yu; Guijin Fan; Xianquan Zhang. 2021. "Reversible data hiding for encrypted image based on adaptive prediction error coding." IET Image Processing 15, no. 11: 2643-2655.

Research article
Published: 30 April 2021 in Security and Communication Networks
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Multimedia hashing is a useful technology of multimedia management, e.g., multimedia search and multimedia security. This paper proposes a robust multimedia hashing for processing videos. The proposed video hashing constructs a high-dimensional matrix via gradient features in the discrete wavelet transform (DWT) domain of preprocessed video, learns low-dimensional features from high-dimensional matrix via multidimensional scaling, and calculates video hash by ordinal measures of the learned low-dimensional features. Extensive experiments on 8300 videos are performed to examine the proposed video hashing. Performance comparisons reveal that the proposed scheme is better than several state-of-the-art schemes in balancing the performances of robustness and discrimination.

ACS Style

Zhenjun Tang; Shaopeng Zhang; Zhenhai Chen; Xianquan Zhang. Robust Video Hashing Based on Multidimensional Scaling and Ordinal Measures. Security and Communication Networks 2021, 2021, 1 -11.

AMA Style

Zhenjun Tang, Shaopeng Zhang, Zhenhai Chen, Xianquan Zhang. Robust Video Hashing Based on Multidimensional Scaling and Ordinal Measures. Security and Communication Networks. 2021; 2021 ():1-11.

Chicago/Turabian Style

Zhenjun Tang; Shaopeng Zhang; Zhenhai Chen; Xianquan Zhang. 2021. "Robust Video Hashing Based on Multidimensional Scaling and Ordinal Measures." Security and Communication Networks 2021, no. : 1-11.

Special issue paper
Published: 20 October 2020 in Multimedia Systems
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Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction.

ACS Style

Xiaoping Liang; Zhenjun Tang; Xiaolan Xie; Jingli Wu; Xianquan Zhang. Robust and fast image hashing with two-dimensional PCA. Multimedia Systems 2020, 27, 389 -401.

AMA Style

Xiaoping Liang, Zhenjun Tang, Xiaolan Xie, Jingli Wu, Xianquan Zhang. Robust and fast image hashing with two-dimensional PCA. Multimedia Systems. 2020; 27 (3):389-401.

Chicago/Turabian Style

Xiaoping Liang; Zhenjun Tang; Xiaolan Xie; Jingli Wu; Xianquan Zhang. 2020. "Robust and fast image hashing with two-dimensional PCA." Multimedia Systems 27, no. 3: 389-401.

Journal article
Published: 21 September 2020 in IEEE Signal Processing Letters
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Due to the deteriorated quality of feature in the propagation process of the neural network, it may be hard for traditional detector to identify a small object by just utilizing information within one region proposal. To overcome the limitation of the traditional object detector, we proposed a graph based relation-aware network, to capture the relation information from labels and images. The semantic relation network is proposed to mine the global semantic relation in labels, and the spatial relation network is proposed to capture the local spatial relation in images. The feature representation is further improved by aggregating the outputs of the two networks. Instead of directly disseminating visual features in the network, the relation-aware network explores more advanced feature information. Experiments on the PASCAL VOC and MS COCO datasets demonstrate that key relation information significantly improve the performance of object detection with better ability to detect small objects and reasonable bounding box. The results on COCO dataset demonstrate our method can detect objects robustly, increasing the detection performance of small objects from average precision and average recall by 31.8% and 32.3% respectively in performance relative to Faster R-CNN.

ACS Style

Shengjia Chen; Zhixin Li; Zhenjun Tang. Relation R-CNN: A Graph Based Relation-Aware Network for Object Detection. IEEE Signal Processing Letters 2020, 27, 1680 -1684.

AMA Style

Shengjia Chen, Zhixin Li, Zhenjun Tang. Relation R-CNN: A Graph Based Relation-Aware Network for Object Detection. IEEE Signal Processing Letters. 2020; 27 (99):1680-1684.

Chicago/Turabian Style

Shengjia Chen; Zhixin Li; Zhenjun Tang. 2020. "Relation R-CNN: A Graph Based Relation-Aware Network for Object Detection." IEEE Signal Processing Letters 27, no. 99: 1680-1684.

Research article
Published: 01 September 2020 in Wireless Communications and Mobile Computing
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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 L2 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.

ACS Style

Zhenjun Tang; Zixuan Yu; Zhixin Li; Chunqiang Yu; Xianquan Zhang. Robust Image Hashing with Low-Rank Representation and Ring Partition. Wireless Communications and Mobile Computing 2020, 2020, 1 -12.

AMA Style

Zhenjun Tang, Zixuan Yu, Zhixin Li, Chunqiang Yu, Xianquan Zhang. Robust Image Hashing with Low-Rank Representation and Ring Partition. Wireless Communications and Mobile Computing. 2020; 2020 ():1-12.

Chicago/Turabian Style

Zhenjun Tang; Zixuan Yu; Zhixin Li; Chunqiang Yu; Xianquan Zhang. 2020. "Robust Image Hashing with Low-Rank Representation and Ring Partition." Wireless Communications and Mobile Computing 2020, no. : 1-12.

Journal article
Published: 08 June 2020 in EURASIP Journal on Image and Video Processing
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Image hashing is an efficient technology for processing digital images and has been successfully used in image copy detection, image retrieval, image authentication, image quality assessment, and so on. In this paper, we design a new image hashing with compressed sensing (CS) and ordinal measures. This hashing method uses a visual attention model called Itti model and Canny operator to construct an image representation, and exploits CS to extract compact features from the representation. Finally, the CS-based compact features are quantized via ordinal measures. L2 norm is used to judge similarity of hashes produced by the proposed hashing method. Experiments about robustness validation, discrimination test, block size discussion, selection of visual attention model, selection of quantization scheme, and effectiveness of the use of ordinal measures are conducted to verify performances of the proposed hashing method. Comparisons with some state-of-the-art algorithms are also carried out. The results illustrate that the proposed hashing method outperforms some compared algorithms in classification according to ROC (receiver operating characteristic) graph.

ACS Style

Zhenjun Tang; Hanyun Zhang; Shenglian Lu; Heng Yao; Xianquan Zhang. Robust image hashing with compressed sensing and ordinal measures. EURASIP Journal on Image and Video Processing 2020, 2020, 1 -20.

AMA Style

Zhenjun Tang, Hanyun Zhang, Shenglian Lu, Heng Yao, Xianquan Zhang. Robust image hashing with compressed sensing and ordinal measures. EURASIP Journal on Image and Video Processing. 2020; 2020 (1):1-20.

Chicago/Turabian Style

Zhenjun Tang; Hanyun Zhang; Shenglian Lu; Heng Yao; Xianquan Zhang. 2020. "Robust image hashing with compressed sensing and ordinal measures." EURASIP Journal on Image and Video Processing 2020, no. 1: 1-20.

Journal article
Published: 18 March 2020 in Applied Sciences
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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.

ACS Style

Dewang Wang; Xianquan Zhang; Chunqiang Yu; Zhenjun Tang. Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy. Applied Sciences 2020, 10, 2058 .

AMA Style

Dewang Wang, Xianquan Zhang, Chunqiang Yu, Zhenjun Tang. Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy. Applied Sciences. 2020; 10 (6):2058.

Chicago/Turabian Style

Dewang Wang; Xianquan Zhang; Chunqiang Yu; Zhenjun Tang. 2020. "Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy." Applied Sciences 10, no. 6: 2058.

Journal article
Published: 06 January 2020 in IEEE Access
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ACS Style

Zhenjun Tang; Hewang Nie; Chi-Man Pun; Heng Yao; Chunqiang Yu; Xianquan Zhang. Color Image Reversible Data Hiding With Double-Layer Embedding. IEEE Access 2020, 8, 6915 -6926.

AMA Style

Zhenjun Tang, Hewang Nie, Chi-Man Pun, Heng Yao, Chunqiang Yu, Xianquan Zhang. Color Image Reversible Data Hiding With Double-Layer Embedding. IEEE Access. 2020; 8 ():6915-6926.

Chicago/Turabian Style

Zhenjun Tang; Hewang Nie; Chi-Man Pun; Heng Yao; Chunqiang Yu; Xianquan Zhang. 2020. "Color Image Reversible Data Hiding With Double-Layer Embedding." IEEE Access 8, no. : 6915-6926.

Journal article
Published: 24 December 2019 in Signal Processing
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Dual-image reversible data hiding (RDH) technology has been an active research area and became an essential part of information security because of its unique advantages in security, embedding capacity and visual quality. For a dual-image RDH strategy, message data is embedded into a cover image to generate two marked images with similar visual quality. In this study, we propose an improved dual-image RDH algorithm based on a prediction-error shift strategy. As a more compact image feature, prediction error is exploited to make better use of the redundancy of image content with the maximum distortion of one. A bidirectional-shift strategy is used to extend the shiftable positions in the central zone of the allowable coordinates. Next, we design a new shiftable-position model for the prediction error corresponding to the peak value in the prediction-error histogram to embed more message bits without causing more distortion. The experimental results demonstrate the efficacy and high-fidelity of the proposed method, especially in relatively low embedding rate circumstances. The average peak signal-to-noise ratio gain of the proposed method on eight standard test images is at least 1.15 dB for the given embedding capacity of 50,000 bits compared with other state-of-the-art dual-image RDH methods.

ACS Style

Heng Yao; Fanyu Mao; Zhenjun Tang; Chuan Qin. High-fidelity dual-image reversible data hiding via prediction-error shift. Signal Processing 2019, 170, 107447 .

AMA Style

Heng Yao, Fanyu Mao, Zhenjun Tang, Chuan Qin. High-fidelity dual-image reversible data hiding via prediction-error shift. Signal Processing. 2019; 170 ():107447.

Chicago/Turabian Style

Heng Yao; Fanyu Mao; Zhenjun Tang; Chuan Qin. 2019. "High-fidelity dual-image reversible data hiding via prediction-error shift." Signal Processing 170, no. : 107447.

Journal article
Published: 20 August 2019 in The Computer Journal
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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.

ACS Style

Zhenjun Tang; Lv Chen; Heng Yao; Xianquan Zhang; Chunqiang Yu. Video Hashing with DCT and NMF. The Computer Journal 2019, 63, 1017 -1030.

AMA Style

Zhenjun Tang, Lv Chen, Heng Yao, Xianquan Zhang, Chunqiang Yu. Video Hashing with DCT and NMF. The Computer Journal. 2019; 63 (7):1017-1030.

Chicago/Turabian Style

Zhenjun Tang; Lv Chen; Heng Yao; Xianquan Zhang; Chunqiang Yu. 2019. "Video Hashing with DCT and NMF." The Computer Journal 63, no. 7: 1017-1030.

Special issue paper
Published: 05 August 2019 in Journal of Real-Time Image Processing
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Reversible image authentication (RIA) is an emerging research field for image tampering operation detection. Tampered regions can be localized precisely by embedding an authentication code (AC) into each divided image block in advance. Once the image is identified as an authentic image, the original image can be recovered without any loss. Under these two preconditions, an efficient RIA scheme is proposed to further improve the detection precision of the final authentication results. Compared with existing methods, a uniform embedding strategy is adopted in this paper, in which one AC bit is embedded into each divided image block to ensure they have the same authentication capability. To improve the forgery localization precision, the block size is adaptively sought according to the embedding capacity of the image. In addition, during the image authentication process, the embedding parameters and location map information are verified to increase the process’s rigorousness. The experimental results demonstrate the superiority of the detection precision of the proposed method.

ACS Style

Heng Yao; HongBin Wei; Chuan Qin; Zhenjun Tang. A real-time reversible image authentication method using uniform embedding strategy. Journal of Real-Time Image Processing 2019, 17, 41 -54.

AMA Style

Heng Yao, HongBin Wei, Chuan Qin, Zhenjun Tang. A real-time reversible image authentication method using uniform embedding strategy. Journal of Real-Time Image Processing. 2019; 17 (1):41-54.

Chicago/Turabian Style

Heng Yao; HongBin Wei; Chuan Qin; Zhenjun Tang. 2019. "A real-time reversible image authentication method using uniform embedding strategy." Journal of Real-Time Image Processing 17, no. 1: 41-54.

Journal article
Published: 23 April 2019 in Information Sciences
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Recently, reversible data hiding in encrypted image have attracted extensive attentions, which can be applied in secure cloud computing and privacy-preserving image processing. In this paper, a reversible data hiding scheme in encrypted image based on the adaptive encoding strategy is proposed. On the content-owner side, block permutation and stream cipher encryption are applied to mask the contents of original image. Through analyzing the distribution of MSB layers, embeddable blocks are first determined and auxiliary data are then generated by data hider. In order to vacate room for data accommodation, MSB layers of embeddable blocks are adaptively compressed according to occurrence frequency of MSB. Thus, additional data can be embedded into MSB layers of encrypted image together with reversed Huffman codewords and auxiliary data. Based on the availability of encryption key and data-hiding key, the receiver can realize separable operations of data extraction, image decryption and image recovery efficiently. Experimental results demonstrate that, our scheme not only can achieve satisfactory rate-distortion performance, but also can obtain greater embedding rate compared with the state-of-the-art schemes.

ACS Style

Yujie Fu; Ping Kong; Heng Yao; Zhenjun Tang; Chuan Qin. Effective reversible data hiding in encrypted image with adaptive encoding strategy. Information Sciences 2019, 494, 21 -36.

AMA Style

Yujie Fu, Ping Kong, Heng Yao, Zhenjun Tang, Chuan Qin. Effective reversible data hiding in encrypted image with adaptive encoding strategy. Information Sciences. 2019; 494 ():21-36.

Chicago/Turabian Style

Yujie Fu; Ping Kong; Heng Yao; Zhenjun Tang; Chuan Qin. 2019. "Effective reversible data hiding in encrypted image with adaptive encoding strategy." Information Sciences 494, no. : 21-36.

Research article
Published: 15 January 2019 in Security and Communication Networks
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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.

ACS Style

Zhenjun Tang; Ye Yang; Shijie Xu; Chunqiang Yu; Xianquan Zhang. Image Encryption with Double Spiral Scans and Chaotic Maps. Security and Communication Networks 2019, 2019, 1 -15.

AMA Style

Zhenjun Tang, Ye Yang, Shijie Xu, Chunqiang Yu, Xianquan Zhang. Image Encryption with Double Spiral Scans and Chaotic Maps. Security and Communication Networks. 2019; 2019 ():1-15.

Chicago/Turabian Style

Zhenjun Tang; Ye Yang; Shijie Xu; Chunqiang Yu; Xianquan Zhang. 2019. "Image Encryption with Double Spiral Scans and Chaotic Maps." Security and Communication Networks 2019, no. : 1-15.

Journal article
Published: 26 November 2018 in IEEE Access
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Reversible data hiding is an important topic of data hiding. This paper proposes a novel separable and error-free reversible data hiding in an encrypted image based on two-layer pixel errors. Specifically, the proposed scheme divides the original image into a series of non-overlapped blocks and permutes these blocks. Then, a closed Hilbert curve is used for scanning each block to obtain a one-dimensional pixel sequence. The pixels of the sequence are encrypted with key transmission. During data hiding, each non-overlapped block of the encrypted image is scanned in the closed Hilbert order to generate a one-dimensional encrypted pixel sequence. Finally, it exploits the histogram of two-layer adjacent encrypted pixel errors to embed secret data by histogram shifting and generate a marked encrypted image. Many experiments are carried out, and the results demonstrate that the proposed scheme reaches a high payload and outperforms some reversible data hiding schemes in the encrypted image.

ACS Style

Chunqiang Yu; Xianquan Zhang; Zhenjun Tang; Xiaojun Xie; Andxiaojun Xie. Separable and Error-Free Reversible Data Hiding in Encrypted Image Based on Two-Layer Pixel Errors. IEEE Access 2018, 6, 76956 -76969.

AMA Style

Chunqiang Yu, Xianquan Zhang, Zhenjun Tang, Xiaojun Xie, Andxiaojun Xie. Separable and Error-Free Reversible Data Hiding in Encrypted Image Based on Two-Layer Pixel Errors. IEEE Access. 2018; 6 ():76956-76969.

Chicago/Turabian Style

Chunqiang Yu; Xianquan Zhang; Zhenjun Tang; Xiaojun Xie; Andxiaojun Xie. 2018. "Separable and Error-Free Reversible Data Hiding in Encrypted Image Based on Two-Layer Pixel Errors." IEEE Access 6, no. : 76956-76969.

Special issue paper
Published: 24 November 2018 in Journal of Real-Time Image Processing
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Reversible data hiding in encrypted image (RDH-EI) is a hot topic of data hiding in recent years. Most RDH-EI algorithms do not reach desirable embedding rate and their computational costs are not suitable for real-time applications. Aiming at these problems, we propose an efficient RDH-EI algorithm with shifting block histogram of pixel differences in homomorphic encrypted domain. A key step of our RDH-EI algorithm is the block-based encryption scheme with additive homomorphism, which can preserve spatial correlation of plaintext image in homomorphic encrypted domain. In addition, our proposed technique of shifting block histogram can achieve efficient data embedding with high payload and correctly recover image. Extensive experiments are conducted to validate performance of our RDH-EI algorithm. Comparison results illustrate that our RDH-EI algorithm outperforms some state-of-the-art algorithms in terms of embedding rate, visual quality and computational time.

ACS Style

Zhenjun Tang; Shijie Xu; Dengpan Ye; Jinyan Wang; Xianquan Zhang; Chuanqiang Yu. Real-time reversible data hiding with shifting block histogram of pixel differences in encrypted image. Journal of Real-Time Image Processing 2018, 16, 709 -724.

AMA Style

Zhenjun Tang, Shijie Xu, Dengpan Ye, Jinyan Wang, Xianquan Zhang, Chuanqiang Yu. Real-time reversible data hiding with shifting block histogram of pixel differences in encrypted image. Journal of Real-Time Image Processing. 2018; 16 (3):709-724.

Chicago/Turabian Style

Zhenjun Tang; Shijie Xu; Dengpan Ye; Jinyan Wang; Xianquan Zhang; Chuanqiang Yu. 2018. "Real-time reversible data hiding with shifting block histogram of pixel differences in encrypted image." Journal of Real-Time Image Processing 16, no. 3: 709-724.

Research article
Published: 04 September 2018 in Security and Communication Networks
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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.

ACS Style

Chunqiang Yu; Xianquan Zhang; Zhenjun Tang; Yan Chen; Jingyu Huang. Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image. Security and Communication Networks 2018, 2018, 1 -13.

AMA Style

Chunqiang Yu, Xianquan Zhang, Zhenjun Tang, Yan Chen, Jingyu Huang. Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image. Security and Communication Networks. 2018; 2018 ():1-13.

Chicago/Turabian Style

Chunqiang Yu; Xianquan Zhang; Zhenjun Tang; Yan Chen; Jingyu Huang. 2018. "Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image." Security and Communication Networks 2018, no. : 1-13.

Journal article
Published: 01 September 2018 in Neurocomputing
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Color vector angle (CVA) is an important feature of processing color images and has been successfully developed and used in real applications, such as edge detection, indexing and retrieval of images. However, it is unsolved how to apply the CVA to efficiently generating an image hash. Also, most image hashing algorithms choose luminance component of color image for hash generation and cannot well capture the color information of images. To tackle these issues, we study efficient image hashing algorithms with the histogram of CVAs, called HCVA hashing. The histogram is first extracted from those angles that are in the biggest circle inscribed inside the normalized image. And then, it is compressed to make a short hash. We conducted some experiments to assess the performance, and illustrated that the DCT (Discrete Cosine Transform) is the best one of that cooperating with HCVA at generating hashes, as well as the HCVA hashing is robust and promising.

ACS Style

Zhenjun Tang; Xuelong Li; Xianquan Zhang; Shichao Zhang; Yumin Dai. Image hashing with color vector angle. Neurocomputing 2018, 308, 147 -158.

AMA Style

Zhenjun Tang, Xuelong Li, Xianquan Zhang, Shichao Zhang, Yumin Dai. Image hashing with color vector angle. Neurocomputing. 2018; 308 ():147-158.

Chicago/Turabian Style

Zhenjun Tang; Xuelong Li; Xianquan Zhang; Shichao Zhang; Yumin Dai. 2018. "Image hashing with color vector angle." Neurocomputing 308, no. : 147-158.

Article
Published: 28 August 2018 in Multimedia Tools and Applications
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This paper proposes a novel reversible data hiding (RDH) algorithm with differential compression (DC) in encrypted image, which has high embedding capacity. The key contributions are two sides. (1) An efficient block-based encryption scheme is developed for encrypting image. It can transfer spatial correlation between neighboring pixels of plaintext image into encrypted image. (2) The DC scheme is proposed to conduct compression of encrypted image. It can efficiently compress encrypted image by exploiting pixel correlation and vacate a large room for data embedding. Many experiments are conducted to evaluate the performance of our RDH algorithm. Comparisons illustrate that our RDH algorithm outperforms some state-of-the-art algorithms in embedding capacity and computational time.

ACS Style

Zhenjun Tang; Shijie Xu; Heng Yao; Chuan Qin; Xianquan Zhang. Reversible data hiding with differential compression in encrypted image. Multimedia Tools and Applications 2018, 78, 9691 -9715.

AMA Style

Zhenjun Tang, Shijie Xu, Heng Yao, Chuan Qin, Xianquan Zhang. Reversible data hiding with differential compression in encrypted image. Multimedia Tools and Applications. 2018; 78 (8):9691-9715.

Chicago/Turabian Style

Zhenjun Tang; Shijie Xu; Heng Yao; Chuan Qin; Xianquan Zhang. 2018. "Reversible data hiding with differential compression in encrypted image." Multimedia Tools and Applications 78, no. 8: 9691-9715.

Journal article
Published: 17 May 2018 in IEEE Transactions on Knowledge and Data Engineering
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This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to 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 14551 images selected for our experiments. 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.

ACS Style

Zhenjun Tang; Lv Chen; Xianquan Zhang; Shichao Zhang. Robust Image Hashing with Tensor Decomposition. IEEE Transactions on Knowledge and Data Engineering 2018, 31, 549 -560.

AMA Style

Zhenjun Tang, Lv Chen, Xianquan Zhang, Shichao Zhang. Robust Image Hashing with Tensor Decomposition. IEEE Transactions on Knowledge and Data Engineering. 2018; 31 (3):549-560.

Chicago/Turabian Style

Zhenjun Tang; Lv Chen; Xianquan Zhang; Shichao Zhang. 2018. "Robust Image Hashing with Tensor Decomposition." IEEE Transactions on Knowledge and Data Engineering 31, no. 3: 549-560.