DCT coefficient dependent quantization table modification steganographic algorithm

Senthooran, V.; Ranathunga, L.

doi:10.1109/cnsc.2014.6906644

Cited by 6 publications

(4 citation statements)

References 10 publications

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“…Next, according to the research conducted by V. Senthooran et al [12], it provides a novel data concealing strategy that is based on the values of the modified quantization table and the DCT coefficients. According to the mathematical formula, the embedding strength of each coefficient is obtained by comparing the suitable quantization table value and DCT coefficients in the correct sequence.…”

Section: Review Of Previous Researchesmentioning

confidence: 99%

Image Steganography:

Abd Aziz,

Mohd Sultan,

Mohamad Zulkufli

2024

IJPCC

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Steganography is the practice of hiding secret information within other media, such as images, audio, video, and text. It has become increasingly important in today's society as a way to enable private and secure communication. This research project focuses on image steganography techniques that are used to evade detection of the secret message through statistical steganalysis techniques. The aim of this research is to compare and evaluate different image steganography methods, study their implementation complexity, and propose a framework to improve current approaches. The research will provide a comparison of the efficiency of different steganography techniques in avoiding detection by steganalysis and may lead to the development of better steganography techniques in the future. This paper focuses on the three steganography methods in the spatial domain: Least Significant Bit (LSB), Pixel-Value Difference (PVD) and Edge-based Data Embedding (EBE) methods. A simple experiment has been conducted to encrypt several images using these three methods, and the distortion measure for LSB using mean square error (MSE) and peak-to-noise ratio (PSNR) has been investigated. Although the distortion measure result is considered acceptable for LSB method in the experiment, all methods resulted in significant difference in the file capacity. This shows that further enhancement in the security of the encryption is necessary so that the secret message will not be easily discovered. Thus, in this paper, we propose a conceptualized enhancement in the security using Morse Code, Base 64, SHA-245, and Advanced Encryption Standard (AES) before encrypting using PVD.

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Section: Review Of Previous Researchesmentioning

confidence: 99%

Image Steganography:

Abd Aziz,

Mohd Sultan,

Mohamad Zulkufli

2024

IJPCC

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“…Furthermore, the results of the proposed method for frequency domain embedding have been compared with both the widely recognized Jsteg method and the method proposed by Senthooran and Ranathunga [27]. For comparison purposes, the same images and payloads used in [27] were utilized. The images involved in this comparison can be seen in Figure 9.…”

Section: Performance Metric On Spatial and Transform Domainmentioning

confidence: 99%

Linear Feedback Shift Registers-Based Randomization for Image Steganography

Hussein,

Al-Momen

2023

Iraqi Journal of Science

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Steganography involves concealing information by embedding data within cover media and it can be categorized into two main domains: spatial and frequency. This paper presents two distinct methods. The first is operating in the spatial domain which utilizes the least significant bits (LSBs) to conceal a secret message. The second method is the functioning in the frequency domain which hides the secret message within the LSBs of the middle-frequency band of the discrete cosine transform (DCT) coefficients. These methods enhance obfuscation by utilizing two layers of randomness: random pixel embedding and random bit embedding within each pixel. Unlike other available methods that embed data in sequential order with a fixed amount. These methods embed the data in a random location with a random amount, further enhancing the level of obfuscation. A pseudo-random binary key that is generated through a nonlinear combination of eight Linear Feedback Shift Registers (LFSRs) controls this randomness. The experimentation involves various 512x512 cover images. The first method achieves an average PSNR of 43.5292 with a payload capacity of up to 16% of the cover image. In contrast, the second method yields an average PSNR of 38.4092 with a payload capacity of up to 8%. The performance analysis demonstrates that the LSB-based method can conceal more data with less visibility, however, it is vulnerable to simple image manipulation. On the other hand, the DCT-based method offers lower capacity with increased visibility, but it is more robust.

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“…Thenmozhi and Chandrasekaran [12] use integer wavelet transform along with cropping. Senthooran and Ranathunga [13] apply DCT coefficients and modified quantization table values. Mazumder and Hemachandran [14] use DWT and change the stegomessage by optimizing message dispersion.…”

Section: Related Workmentioning

confidence: 99%

Image steganography based on color palette transformation in color space

Margalikas

Ramanauskaitė

2019

J Image Video Proc.

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In this paper, we present a novel image steganography method which is based on color palette transformation in color space. Most of the existing image steganography methods modify separate image pixels, and random noise appears in the image. By proposing a method, which changes the color palette of the image (all pixels of the same color will be changed to the same color), we achieve a higher user perception. Presented comparison of stegoimage quality metrics with other image steganography methods proved the new method is one of the best according to Structural Similarity Index (SSIM) and Peak Signal Noise Ratio (PSNR) values. The capability is average among other methods, but our method has a bigger capacity among methods with better SSIM and PSNR values. The color and pixel capability can be increased by using standard or adaptive color palette images with smoothing, but it will increase the embedding identification possibility.

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DCT coefficient dependent quantization table modification steganographic algorithm

Cited by 6 publications

References 10 publications

Image Steganography:

Image Steganography:

Linear Feedback Shift Registers-Based Randomization for Image Steganography

Image steganography based on color palette transformation in color space

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