En-VStegNET: Video Steganography using spatio-temporal feature enhancement with 3D-CNN and Hourglass

Jaiswal, Abhishek; Kumar, Suraj; Nigam, Aditya

doi:10.1109/ijcnn48605.2020.9206921

Cited by 12 publications

(18 citation statements)

References 21 publications

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“…AI methods are optimal and provide adaptive solutions. However, AI methods are used in the field of image steganography method [110,111]and only a handful of research [35,79,123] has focused on video steganography. Due to the generality of image steganography, the AI technology applied to image steganography has great reference value for video steganography.…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

Video steganography: recent advances and challenges

Kunhoth

Subramanian

Al‐Maadeed

et al. 2023

Multimed Tools Appl

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Video steganography approach enables hiding chunks of secret information inside video sequences. The features of video sequences including high capacity as well as complex structure make them more preferable for choosing as cover media over other media such as image, text, or audio. Video steganography is a prominent as well as the evolving field in the information security domain and significant number of video steganography methods are proposed in recent years. This article provides a comprehensive review of video steganography methods proposed in the literature. This article initially reviews various raw domain-based video steganography methods. In particular, the raw domain-based methods include spatial domain approaches such as least significant bits (LSB), transform domain-based methods such as discrete wavelet transform, discrete cosine transform, etc. Furthermore, the article looks into various compressed domain steganography methods. A critical comparative analysis is included in the article to analyze and contrast the steganography methods proposed in the literature. A brief description of various evaluation matrices for video steganography methods is provided in this article. Moreover, a brief introduction to steganalysis and video steganalysis is provided. The article concludes with a discussion focused on the limitations and challenges of the video steganography methods. Further, a brief insight into future directions in video steganography systems is provided.

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Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

Video steganography: recent advances and challenges

Kunhoth

Subramanian

Al‐Maadeed

et al. 2023

Multimed Tools Appl

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“…The embedding capacity of our algorithm is 24 bpp (Bits Per Pixel) because we follow the experiment setting of many state-of-the-art video steganography methods (VStegNET [11] and En-VStegNET [26]) that conceals eight secret frames into eight cover frames (each pixel in the cover frame hides a complete RGB colour of the secret frame). To study the capacity of the proposed DRANet, we conduct an extra experiment with modified numbers of secret frames (10, 16, 20 and 24).…”

Section: Embedding Capacitymentioning

confidence: 99%

“… Illustration of our spatio‐temporal attention. Top: Given an input cover video, we conduct the state‐of‐the‐art methods, (b) HIiPS [9], (c) HCCVS [10], and (d) VStegNET [11], to perform video steganography. Bottom: We show the residuals between the cover and container frames on different methods.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast to the HCCVS approach which uses 2D‐convolutions, Kumar et al. proposed VStegNET [11] that extracts spatio‐temporal features by using 3D‐CNN directly. To reduce the number of parameters, we propose a novel Deeply‐Recursive Attention Network (DRANet) that performs similar convolution‐based operations on different frames recursively.…”

Section: Introductionmentioning

confidence: 99%

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Deeply‐Recursive Attention Network for video steganography

Cui

Zheng

et al. 2023

CAAI Trans on Intel Tech

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Video steganography plays an important role in secret communication that conceals a secret video in a cover video by perturbing the value of pixels in the cover frames. Imperceptibility is the first and foremost requirement of any steganographic approach. Inspired by the fact that human eyes perceive pixel perturbation differently in different video areas, a novel effective and efficient Deeply‐Recursive Attention Network (DRANet) for video steganography to find suitable areas for information hiding via modelling spatio‐temporal attention is proposed. The DRANet mainly contains two important components, a Non‐Local Self‐Attention (NLSA) block and a Non‐Local Co‐Attention (NLCA) block. Specifically, the NLSA block can select the cover frame areas which are suitable for hiding by computing the correlations among inter‐ and intra‐cover frames. The NLCA block aims to effectively produce the enhanced representations of the secret frames to enhance the robustness of the model and alleviate the influence of different areas in the secret video. Furthermore, the DRANet reduces the model parameters by performing similar operations on the different frames within an input video recursively. Experimental results show the proposed DRANet achieves better performance with fewer parameters than the state‐of‐the‐art competitors.

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“…The majority of deep steganography approaches concentrate on hiding binary messages [6,2,7] or natural images [1,3]. While some efforts have been made to use deep steganography to hide video [8,9] or audio [10] within the same data type, there is a short- age of research on cross-modal steganography. This type of steganography involves hiding secret data in cover data that is in a different format than the secret data.…”

Section: Introductionmentioning

confidence: 99%

School Violence in South Korea

Han¹

2021

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Steganography is the process of embedding secret data into another message or data, in such a way that it is not easily noticeable. With the advancement of deep learning, Deep Neural Networks (DNNs) have recently been utilized in steganography. However, existing deep steganography techniques are limited in scope, as they focus on specific data types and are not effective for cross-modal steganography. Therefore, We propose a deep cross-modal steganography framework using Implicit Neural Representations (INRs) to hide secret data of various formats in cover images. The proposed framework employs INRs to represent the secret data, which can handle data of various modalities and resolutions. Experiments on various secret datasets of diverse types demonstrate that the proposed approach is expandable and capable of accommodating different modalities.

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En-VStegNET: Video Steganography using spatio-temporal feature enhancement with 3D-CNN and Hourglass

Cited by 12 publications

References 21 publications

Video steganography: recent advances and challenges

Video steganography: recent advances and challenges

Deeply‐Recursive Attention Network for video steganography

School Violence in South Korea

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