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StegVRN: Enhancing Quality of Video Steganography Using CNN-Based Object Selection

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Abstract

The dynamic nature of video content facilitates steganographic processes, and recent advancements in Deep Learning, particularly Convolutional Neural Networks (CNNs), have led to the development of new steganographic techniques. The main contribution lies in using CNN-based deep learning techniques for object identification, with a particular focus on leveraging the full potential of ResNet and VGGNet architectures to achieve superior object classification results. In this paper, StegVRN-a deep learning model for object detection and data embedding using edge detection technique is proposed. StegVRN comprises 2 models: the first model, VEDS (VGGNet-based Edge Detection Steganography), employs the VGGNet architecture for object detection and utilizes an edge detection algorithm for data embedding. The second model, named REDS (ResNet-based Edge Detection Steganography), is based on the ResNet architecture and incorporates an edge detection algorithm for object detection and data embedding. Both models have been applied with three different edge detection techniques, which are Canny, Sobel, and Haar, for data embedding. Experimental results demonstrate that the StegVRN-based steganography framework is easy to optimize, and increasing the depth of layers in ResNet can enhance accuracy while reducing loss. STEGVRN has been evaluated using the Imagenet 1000 dataset for secret images and the UCF101 dataset for cover videos. The comparison with other state-of-the-art methods is based on stego quality metrics, such as MSE, PSNR, and SSIM. The experimental outcomes demonstrate that STEGVRN outperforms other steganography methodologies in terms of stego video perceptual quality, secret data decoding accuracy, and embedding capacity.

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Data availability

The ImageNet1000 Dataset used to select secret images is freely downloadable from Kaggle.com. The UCF101 dataset from which cover videos are selected is freely downloadable from official website http://crcv.ucf.edu/data/UCF101.php.

Abbreviations

Conv:

Convolutional layer

ReLU:

Rectified linear unit activation function

Max-Pooling:

Max-pooling layer

FC:

Fully connected layer

Softmax:

Softmax activation function

X:

Input image

Z:

Output activation

W:

Weights of the layer

A:

Activation

Fh:

Height of the filter

Fw:

Width of the filter

P:

Max-pooling operation

s:

Stride

b:

Bias vector

K:

Total number of classes

P(y = j|X):

Probability of input X belonging to class j

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Acknowledgements

This work is funded by Vision Group of Technology (VGST), Government of Karnataka, India under CISEE scheme. The authors would like to express gratitude to Management and Staff of JNN College of Engineering and VGST for their invaluable support.

Funding

The work of G. R. Manjula was funded by Vision Group on Science and Technology, GRD-749.

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  1. JNNCE Shivamogga, Visvesvaraya Technological University, Belagavi, Karnataka, India

    R. B. Sushma & G. R. Manjula

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  1. R. B. Sushma
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We wish to confirm that there are no known conflicts of interest associated with this publication and there is a financial support for this research work from Vision Group of Science and Technology (VGST), Karnataka, India. The organization supports research work of academicians through funding. There is no objection from VGST and JNNCE regarding publishing the research work. We confirm that the manuscript has been read and approved by all named authors and that there are no other persons who satisfied the criteria for authorship but are not listed. We further confirm that the order of authors listed in the manuscript has been approved by all of us. We confirm that we have given due consideration to the protection of intellectual property associated with this work and that there are no impediments to publication, including the timing of publication, with respect to intellectual property. In so doing, we confirm that we have followed the regulations of our institutions concerning intellectual property. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He/she is responsible for communicating with the other authors about progress, submissions of revisions, and final approval of proofs. We confirm that we have provided a current, correct email address which is accessible by the corresponding author.

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Sushma, R.B., Manjula, G.R. StegVRN: Enhancing Quality of Video Steganography Using CNN-Based Object Selection. SN COMPUT. SCI. 5, 227 (2024). https://doi.org/10.1007/s42979-023-02498-2

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