An Effective Video Steganography Method for Biometric Identification

Lu, Yingqi; Lu, Cheng; Qi, Miao

doi:10.1007/978-3-642-13577-4_42

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…The existing methods [74,82,87,104] have utilized conventional methods for detecting moving objects or skin lesions. The latest deep convolutional neural network-based methods can be employed in the future for effectively detecting moving objects and skin lesions.…”

Section: Discussion Challenges and Future Directionsmentioning

confidence: 99%

“…Owing to the fact that the adaptive steganography technique can provide additional security and robustness, few adaptive steganography approaches based on the wavelet domain are proposed in the literature. Lu et al [74] proposed an adaptive technique to hide the biometric data in the frequency subbands of the video frames. The suitable frame and regions of interest inside the frame for embedding the data are selected by implementing a motion analysis technique.…”

Section: (X Y)mentioning

confidence: 99%

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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%

Section: (X Y)mentioning

confidence: 99%

Video steganography: recent advances and challenges

Kunhoth

Subramanian

Al‐Maadeed

et al. 2023

Multimed Tools Appl

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show abstract

“…For example, Wengrowski et al [40] develop a deep photographic steganography network to obscure light field messaging in the video. Besides, Lu et al [23] hide an image set in videos to protect the biometric data during transmission for secure personal identification. Noteworthy, various audio communication solutions have been widely applied in the industry, making audio data a suitable type of cover media.…”

Section: Cross-modal Steganographymentioning

confidence: 99%

Materials chemistry at Nankai University: A special issue dedicated to the 100th anniversary of Nankai University

Zhao

et al. 2019

Sci. China Mater.

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Proactive failure detection of instances is vitally essential to microservice systems because an instance failure can propagate to the whole system and degrade the system's performance. Over the years, many single-modal (i.e., metrics, logs, or traces) data-based anomaly detection methods have been proposed. However, they tend to miss a large number of failures and generate numerous false alarms because they ignore the correlation of multimodal data. In this work, we propose AnoFusion, an unsupervised failure detection approach, to proactively detect instance failures through multimodal data for microservice systems. It applies a Graph Transformer Network (GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to address the challenges introduced by dynamically changing multimodal data. We evaluate the performance of AnoFusion through two datasets, demonstrating that it achieves the 𝐹 1 -score of 0.857 and 0.922, respectively, outperforming the state-of-the-art failure detection approaches. CCS CONCEPTS• Software and its engineering → Maintaining software; • Computing methodologies → Failure Detection; Graph Neural Networks.

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“…Most of the work in Steganography has been done on images [11,17,48,45], video clips [6,9,59,45,47], music, sounds [10,16,45,60] and texts [4,28,7,33,34,35,43,45,54]. Text-based Steganography is the most complex due to the lack of redundant information.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial-based Steganographic Scheme for PDF File Justified Texts

Ekodeck¹,

Ebele²,

Ndoundam³

2022

Preprint

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In this paper, we proposed a method that exploits justified texts of PDF files to embed secret data. The method is inspired by the work of Khosravi et al on "A new method for pdf steganography in justified texts" where they proposed a stegosystem allowing to embed in particular lines called a host line, 4 bits of secret message. We've improved it by exposing a combinatorics-based method able not only to embed 7 bits of secret message in these host lines but also b bits (b<14) in any line containing at least n spaces and m added spaces for (m, n > 0), thus allowing a sender to conceal a bigger message with very few changes made to the cover text.<br>

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An Effective Video Steganography Method for Biometric Identification

Cited by 8 publications

References 12 publications

Video steganography: recent advances and challenges

Video steganography: recent advances and challenges

Materials chemistry at Nankai University: A special issue dedicated to the 100th anniversary of Nankai University

Combinatorial-based Steganographic Scheme for PDF File Justified Texts

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