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  • Open Access

    ARTICLE

    High-Imperceptibility Data Hiding Scheme for JPEG Images Based on Direction Modification

    Li Liu1, Jing Li1, Yingchun Wu1, Chin-Chen Chang2,*, Anhong Wang1

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1415-1432, 2023, DOI:10.32604/csse.2023.040039 - 28 July 2023

    Abstract Data hiding (DH) is an important technology for securely transmitting secret data in networks, and has increasing become a research hotspot throughout the world. However, for Joint photographic experts group (JPEG) images, it is difficult to balance the contradiction among embedded capacity, visual quality and the file size increment in existing data hiding schemes. Thus, to deal with this problem, a high-imperceptibility data hiding for JPEG images is proposed based on direction modification. First, this proposed scheme sorts all of the quantized discrete cosine transform (DCT) block in ascending order according to the number of… More >

  • Open Access

    ARTICLE

    A Deep Learning Driven Feature Based Steganalysis Approach

    Yuchen Li1, Baohong Ling1,2,*, Donghui Hu1, Shuli Zheng1, Guoan Zhang3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2213-2225, 2023, DOI:10.32604/iasc.2023.029983 - 21 June 2023

    Abstract The goal of steganalysis is to detect whether the cover carries the secret information which is embedded by steganographic algorithms. The traditional steganalysis detector is trained on the stego images created by a certain type of steganographic algorithm, whose detection performance drops rapidly when it is applied to detect another type of steganographic algorithm. This phenomenon is called as steganographic algorithm mismatch in steganalysis. To resolve this problem, we propose a deep learning driven feature-based approach. An advanced steganalysis neural network is used to extract steganographic features, different pairs of training images embedded with steganographic More >

  • Open Access

    ARTICLE

    Multiple Images Steganography of JPEG Images Based on Optimal Payload Distribution

    Yang Pei1,2, Xiangyang Luo1,2,*, Yi Zhang2, Liyan Zhu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.1, pp. 417-436, 2020, DOI:10.32604/cmes.2020.010636 - 18 September 2020

    Abstract Multiple images steganography refers to hiding secret messages in multiple natural images to minimize the leakage of secret messages during transmission. Currently, the main multiple images steganography algorithms mainly distribute the payloads as sparsely as possible in multiple cover images to improve the detection error rate of stego images. In order to enable the payloads to be accurately and efficiently distributed in each cover image, this paper proposes a multiple images steganography for JPEG images based on optimal payload redistribution. Firstly, the algorithm uses the principle of dynamic programming to redistribute the payloads of the… More >

  • Open Access

    ARTICLE

    Improved Lossless Data Hiding for JPEG Images Based on Histogram Modification

    Yang Du1, Zhaoxia Yin1,2,*, Xinpeng Zhang3

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 495-507, 2018, DOI:10.3970/cmc.2018.02440

    Abstract This paper proposes a lossless and high payload data hiding scheme for JPEG images by histogram modification. The most in JPEG bitstream consists of a sequence of VLCs (variable length codes) and the appended bits. Each VLC has a corresponding RLV (run/length value) to record the AC/DC coefficients. To achieve lossless data hiding with high payload, we shift the histogram of VLCs and modify the DHT segment to embed data. Since we sort the histogram of VLCs in descending order, the filesize expansion is limited. The paper’s key contribution includes: Lossless data hiding, less filesize More >

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