A Modified High Frequency Adaptive Security Approach using Steganography for Region Selection based on Threshold Value

Mungmode, Sachin; Sedamkar, R. R.; Kulkarni, Niranjan

doi:10.1016/j.procs.2016.03.114

Cited by 11 publications

(9 citation statements)

References 10 publications

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“…In the (2), Si,j= Stego-video and Yi,j= Cover video. The two parameters X and Y indicated the total numbers of the pixels of video frames for two dimensions [97]. Therefore, MSE is important to measure to evaluate the quality of stego-video and test its modification rate.…”

Section: Mse and Psnr Measurementsmentioning

confidence: 99%

Video steganography using 3D distance calculator based on YCbCr color components

Baker

Majeed

Alazawi

et al. 2021

IJEECS

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Steganography techniques have taken a major role in the development in the field of transferring multimedia contents and communications. Therefore, field of steganography become interested as the need for security increased significantly. Steganography is a technique to hide information within cover media so that this media does not change significantly. Steganography process in a video is to hide the information from the intruder and prevent him access to that hidden information. This paper presents the algorithm of steganography in the video frames. The proposed algorithm selected the best frames to hide the message in video using 3D distance equation to increasing difficulty onto the intruder to detect and guess the location of the message in the video frames. As well as selected the best frames in this algorithm increased the difficulty and give us the best stego-video quality using structural similarity (SSIM). Also, the hash function was used to generate random positions to hide the message in the lines of video frames. The proposed algorithm evaluated with mean squared error (MSE), peak signalto-noise ratio (PSNR) and SSIM measurement. The results were acceptable and shows that is the difficulty of distinguishing the hidden message in stego-video with the human eye.

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Section: Mse and Psnr Measurementsmentioning

confidence: 99%

Video steganography using 3D distance calculator based on YCbCr color components

Baker

Majeed

Alazawi

et al. 2021

IJEECS

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“…Na técnica do sistema de substituição, os bits redundantes ou desnecessários são subistituidos de uma imagem de cobertura pelos bits da mensagem secreta usando, por exemplo, o método Bit Menos Significativo (LSB, do inglês, Least Significant Bit) para codificar a mensagem secreta. No entanto, a esteganografia do LSB é suscetível as técnicas de análise de esteganização [4]. Para evitar a instalação de detecção de esteganografia através do uso do método LSB, em [5], foi proposta uma nova estratégia de substituição dos bits.…”

Section: Introductionunclassified

Steganography Genetic Algorithm Hyperparameter Tuning through Response Surface Methodology

Silva¹,

Carvalho²,

Martins³

2020

AJCEAM

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Steganography consists of hidding bits of an information source into a host source. In image processing, a common way of doing the hiding process is to break each byte from the message information and embbed into the message bytes in a way that the differences among the original host and the embedded one is minimized. A genetic algorithm can be used to find the proper combination of bits in order to minimize such differences, but some hyperparameters need to be optimized in order to get an optimized performance. This work investigates the application of Response Surface Methodology in order to find the best hyperparameters of a genetic algorithm applied to image steganography.

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“…The main steganographic methods in the spatial domain [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ] are LSB-based (Low Significant Bit). Recently, entropy has also been extensively used to support data-hiding algorithms [ 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors

Battikh

Assad

Hoang

et al. 2019

Entropy

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In this paper, we firstly study the security enhancement of three steganographic methods by using a proposed chaotic system. The first method, namely the Enhanced Edge Adaptive Image Steganography Based on LSB Matching Revisited (EEALSBMR), is present in the spatial domain. The two other methods, the Enhanced Discrete Cosine Transform (EDCT) and Enhanced Discrete Wavelet transform (EDWT), are present in the frequency domain. The chaotic system is extremely robust and consists of a strong chaotic generator and a 2-D Cat map. Its main role is to secure the content of a message in case a message is detected. Secondly, three blind steganalysis methods, based on multi-resolution wavelet decomposition, are used to detect whether an embedded message is hidden in the tested image (stego image) or not (cover image). The steganalysis approach is based on the hypothesis that message-embedding schemes leave statistical evidence or structure in images that can be exploited for detection. The simulation results show that the Support Vector Machine (SVM) classifier and the Fisher Linear Discriminant (FLD) cannot distinguish between cover and stego images if the message size is smaller than 20% in the EEALSBMR steganographic method and if the message size is smaller than 15% in the EDCT steganographic method. However, SVM and FLD can distinguish between cover and stego images with reasonable accuracy in the EDWT steganographic method, irrespective of the message size.

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A Modified High Frequency Adaptive Security Approach using Steganography for Region Selection based on Threshold Value

Cited by 11 publications

References 10 publications

Video steganography using 3D distance calculator based on YCbCr color components

Video steganography using 3D distance calculator based on YCbCr color components

Steganography Genetic Algorithm Hyperparameter Tuning through Response Surface Methodology

Comparative Study of Three Steganographic Methods Using a Chaotic System and Their Universal Steganalysis Based on Three Feature Vectors

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