Adaptive multi-cascaded ResNet-based efficient multimedia steganography framework using hybrid mouth brooding fish-emperor penguin optimization mechanism

IF 0.9 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Computer Security Pub Date : 2024-07-25 DOI:10.3233/jcs-230049

Garikamukkala Vijaya Kiran, Vidhya Krishnan

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引用次数: 0

Abstract

A massive amount of data is transmitted in the Internet of Things (IoT). Nowadays, the concerning of security issues are the major factor while transferring data through wireless networks. Since, data privacy becomes complicated. In this research work, a newly proposed model for multimedia steganography is developed. Initially, the required video is obtained from the publically available datasets, and then the acquired input is subjected to the Adaptive Discrete Cosine Transformation (DCT) based block process. The optimal blocks are chosen by the Adaptive Multi-cascaded ResNet (AMC-ResNet) model for applying stego data. Here, the parameter optimization takes place in the DCT and ResNet model to enhance the steganography performance via the Mouth Brooding Fish Emperor Penguin Optimization (MBFEPO) derived from the Mouth Brooding Fish Algorithm (MBFA) and Emperor Penguin Optimization Algorithm (EPOA). Finally, the inverse DCT is employed at the blocks to get the final stego video. In the audio steganography phase, the wanted audio is gathered from external websites. The collected data are given to the Short-time Fourier Transform (STFT) to convert into the spectrogram image, and then the spectrogram image is given to the Adaptive DCT block, selecting the block to apply stego data. Thus, the blocks are selected with the utilization of the Adaptive Multi-cascaded ResNet (AMC-ResNet), where the parameters within the DCT and the ResNet are optimized via the same MBFEPO to improve the performance. After, the Inverse ADCT is applied to reconstruct the spectrogram image. Then, the resultant stego audio is obtained by using the Inverse STFT. Finally, several experiments are conducted to estimate the working ability of the proposed steganography model. The outcome of the recommended model shows 12.3%, 52.6%, 12.3%, and 84.3% better performance SFO, HBA, MBFA, and EPOA in terms of median. The recommended model performs superior performance rather than the existing approaches.

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基于自适应多级联 ResNet 的高效多媒体隐写术框架，采用鱼-企鹅混合口编码优化机制

物联网（IoT）传输大量数据。如今，在通过无线网络传输数据时，有关安全问题是主要因素。因此，数据隐私变得复杂。在这项研究工作中，开发了一种新提出的多媒体隐写术模型。首先，从公开的数据集中获取所需的视频，然后对获取的输入数据进行基于自适应离散余弦变换（DCT）的块处理。自适应多级联 ResNet（AMC-ResNet）模型选择最佳区块来应用偷窃数据。在此，DCT 和 ResNet 模型中进行了参数优化，以通过从鱼口编码算法（MBFA）和帝企鹅优化算法（EPOA）衍生出的鱼口编码帝企鹅优化算法（MBFEPO）提高隐写性能。最后，在区块中使用反 DCT，得到最终的隐秘视频。在音频隐写阶段，需要从外部网站收集音频。收集到的数据通过短时傅里叶变换（STFT）转换成频谱图图像，然后将频谱图图像交给自适应 DCT 块，选择应用偷窃数据的块。因此，块的选择利用了自适应多级联 ResNet（AMC-ResNet），其中 DCT 和 ResNet 内的参数通过相同的 MBFEPO 进行优化，以提高性能。然后，应用反 ADCT 重构频谱图图像。然后，通过使用反 STFT 来获得偷窃音频。最后，我们进行了多次实验，以评估建议的隐写术模型的工作能力。结果显示，在中位数方面，推荐模型分别比 SFO、HBA、MBFA 和 EPOA 高出 12.3%、52.6%、12.3% 和 84.3%。与现有方法相比，推荐模型的性能更优越。

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来源期刊

Journal of Computer Security COMPUTER SCIENCE, INFORMATION SYSTEMS-

CiteScore

1.70

自引率

0.00%

发文量

期刊介绍： The Journal of Computer Security presents research and development results of lasting significance in the theory, design, implementation, analysis, and application of secure computer systems and networks. It will also provide a forum for ideas about the meaning and implications of security and privacy, particularly those with important consequences for the technical community. The Journal provides an opportunity to publish articles of greater depth and length than is possible in the proceedings of various existing conferences, while addressing an audience of researchers in computer security who can be assumed to have a more specialized background than the readership of other archival publications.