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Showing 3 results for Steganography
Dr Mansoor Fateh, Samira Rajabloo, Elahe Alipour,
Volume 5, Issue 2 (3-2017)
Abstract
In this paper, the image steganography based on LSB and pixel classification is reviewed. Then, the method for steganography information in image is presented. This method based on LSB. Our purpose of this paper is to minimize the changes in cover image. At the first, the pixels of the image are selected to hiding the message; second complemented message will be hidden in LSB of selected pixels. In this paper, to solve some problems LSB method and minimize the changes, pixels categorized based on values of bits of second, third, fourth. In each category, ratio of changed pixels to unchanged pixels is calculated. If the ratio is greater than one, the LSB of that category are reversed and those changes reach at least. Mean Square Error and Peak Signal to Noise Ratio are two criterions to evaluate stego-image quality. PSNR and MSE of proposed method in comparison with simple LSB method, are respectively growth rate 0.13 percent and reduction rate 0.19 percent.
Vajiheh Sabeti , Masomeh Sobhani,
Volume 11, Issue 2 (3-2023)
Abstract
Steganography is the science and art of hiding the existence of communication. In steganography, by hiding information in a digital media, the existence of communication remains hidden from the enemy’s view. The idea of spatial-based adaptive methods is to embed more in the edge areas of the image. In these methods, areas of the image that have more changes are prioritized for embedding. On the other hand, wavelet-based methods perform embedding in high frequency subbands to match the human vision system. The proposed idea in this paper is embedding with higher priority in areas of high frequency subbands resulting from wavelet transform that have many changes. First, according to the length of the data, a threshold value is determined, based on which suitable embedding areas are identified in each high frequency subband, and then the embedding process is performed in them. This process is such that the receiver can extract the data completely by repeating it. The implementation results show that in the proposed method, the use of Integer Wavelet Transform (IWT) is more successful than Discrete Wavelet Transform (DWT). The quality of the resulted stego image is higher and its security is more than the other wavelet-based methods.
Vajiheh Sabeti, Mahdiyeh Samiei,
Volume 12, Issue 2 (2-2024)
Abstract
Steganalysis is the art of detecting the existence of hidden data. Recent research has revealed that convolutional neural networks (CNNs) can detect data through automatic feature extraction. Several studies investigated the performance of existing models using a limited number of spatial steganography methods. This study aims to propose a CNN and comprehensively investigate its efficiency in detecting different spatial methods. The proposed model comprises three modules: preprocessing, convolutional (five blocks), and classifier (three fully connected layers). The test results for the least-significant-bit (LSB) and pixel-value differencing (PVD) based methods indicate that the proposed method can detect data of even concise length with high
accuracy and a low error. The proposed method also detects complexity-based LSB-M (CBL) as an adaptive approach. Lower embedding rates make this success even more impressive. Manual feature extraction has much lower success rates due to low variations of statistical features at low embedding rates than the proposed model.
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