A Formula Adaptive Pixel Pair Matching Steganography Algorithm

Long, Min; Li, Fenfang

doi:10.1155/2018/7682098

Cited by 3 publications

(3 citation statements)

References 17 publications

(20 reference statements)

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“…The test results demonstrated that the proposed strategy enhances the text retrieval at a lower error rate of execution of the C1BT-BME, 1BT-BME, MF-1BT, and it outperforms other low bit-depth based text retrieval [19] techniques at the MB level. The Adaptive Block Matching Steganography Algorithm Using Bit-Plane Matching considers the magnitude as well as the directive data of the pixel elements in the MB [20].…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Improved Search Pattern with Discrete Wavelet Transform for Video Steganography

Atee

Yasari

Hammood

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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The scope of this research is to overcome the computational complexity of the methods for hiding messages inside video frames using various advanced search procedures. For this purpose, the optimal binary coding scheme with low-complexity frame adjustment has been implemented for wireless video transmission applications. To minimize the computational complexity of concealing the existence of messages and the ability to accurately extract them, the Improved Search Pattern with Discrete Wavelet Transform Based Phase Correlation (ISP-DWT-PC) was computed. The approach has also been used to improve the speed of the hidden data retrieval reliably using a simpler search pattern. This paper emphasizes computer forensics procedures, which acts as a tool for data security, reliability, and authentication in video steganography applications works. The results that have been achieved is surpassed the traditional methods in terms of PSNR, MSE, SSIM, Accuracy, surpassed, sensitivity and specificity.

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Section: Literature Reviewmentioning

confidence: 99%

“…The function of the normalized phase correlation of X c K 1 ; K 2 ð Þand X p K 1 ; K 2 ð Þis defined in Eq. (20).…”

Section: Mve 1 Mvementioning

confidence: 99%

Improved Search Pattern with Discrete Wavelet Transform for Video Steganography

Atee

Yasari

Hammood

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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show abstract

“…Often statistical analyses can easily detect the pattern of the altered pixels as shown in (Fridrich, Goljan, and Du 2001;Lerch-Hostalot and Megías 2016;Luo, Huang, and Huang 2010). Recent works have therefore focused on methods that preserve the original image statistics or the design of sophisticated distortion functions (Holub and Fridrich 2012;Holub, Fridrich, and Denemark 2014;Long and Li 2018;Pevnỳ, Filler, and Bas 2010;Swain 2018;Tamimi, Abdalla, and Al-Allaf 2013).…”

Section: Related Workmentioning

confidence: 99%

Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks

Schreyer,

Schulze,

Borth

2020

Preprint

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Nowadays, organizations collect vast quantities of sensitive information in 'Enterprise Resource Planning' (ERP) systems, such as accounting relevant transactions, customer master data, or strategic sales price information. The leakage of such information poses a severe threat for companies as the number of incidents and the reputational damage to those experiencing them continue to increase. At the same time, discoveries in deep learning research revealed that machine learning models could be maliciously misused to create new attack vectors. Understanding the nature of such attacks becomes increasingly important for the (internal) audit and fraud examination practice. The creation of such an awareness holds in particular for the fraudulent data leakage using deep learning-based steganographic techniques that might remain undetected by state-of-the-art 'Computer Assisted Audit Techniques' (CAATs). In this work, we introduce a realworld 'threat model' designed to leak sensitive accounting data. In addition, we show that a deep steganographic process, constituted by three neural networks, can be trained to hide such data in unobtrusive 'day-to-day' images. Finally, we provide qualitative and quantitative evaluations on two publicly available real-world payment datasets.

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High-Capacity Convolutional Video Steganography with Temporal Residual Modeling

Weng

et al. 2019

Proceedings of the 2019 on International Conference on Multimedia Retrieval

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Steganography represents the art of unobtrusively concealing a secrete message within some cover data. The key scope of this work is about visual steganography techniques that hide a full-sized color image / video within another. A majority of existing works are devoted to the image case, where both secret and cover data are images. We empirically validate that image steganography model does not naturally extend to the video case (i.e., hiding a video into another video), mainly because it completely ignores the temporal redundancy within consecutive video frames. Our work proposes a novel solution to the problem of video steganography. The technical contributions are two-fold: first, the residual between two consecutive frames tends to zero at most pixels. Hiding such highly-sparse data is significantly easier than hiding the original frames. Motivated by this fact, we propose to explicitly consider inter-frame residuals rather than blindly applying image steganography model on every video frame. Specifically, our model contains two branches, one of which is specially designed for hiding inter-frame difference into a cover video frame and the other instead hides the original secret frame. A simple thresholding method determines which branch a secret video frame shall choose. When revealing the concealed secret video, two decoders are devised, revealing difference or frame respectively. Second, we develop the model based on deep convolutional neural networks, which is the first of its kind in the literature of video steganography. In experiments, comprehensive evaluations are conducted to compare our model with both classic least significant bit (LSB) method and pure image steganography models. All results strongly suggest that the proposed model enjoys advantages over previous methods. We also carefully investigate key factors in the success of our deep video steganography model. The first two authors contribute equally. Yadong Mu is the corresponding author of this work. cover secret Alice container Eve Bob decoded secret contain secret message? Figure 1: The full scheme of steganography. See main text for more explanation.

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A Formula Adaptive Pixel Pair Matching Steganography Algorithm

Cited by 3 publications

References 17 publications

Improved Search Pattern with Discrete Wavelet Transform for Video Steganography

Improved Search Pattern with Discrete Wavelet Transform for Video Steganography

Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks

High-Capacity Convolutional Video Steganography with Temporal Residual Modeling

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