Relating the Embedding Efficiency of LSB Steganography Techniques in Spatial and Transform Domains

Malathi, P.; Kumar, T. Gireesh

doi:10.1016/j.procs.2016.07.270

Cited by 34 publications

(13 citation statements)

References 7 publications

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“…The Mean squared error (MSE) and Peak signal to noise ratio (PSNR) was used as the performance measure to compare the different LSB techniques. This paper shows that MSE and PSNR of LSB techniques with Matrix embedding yields better results [15].…”

Section: Related Workmentioning

confidence: 75%

An Analysis on LSB Image Steganography with Colour Image as Cover

Chaturvedi¹,

Mohi²

2018

IJCA

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LSB-based steganographic methods embed secret messages into the cover image by directly manipulating the leastsignificant-bit (LSB) plane. Although it is not the best steganographic method, LSB replacement is worth studying because of its large embedding capacity and simplicity. In LSB replacement, the LSB plane is simply replaced with the secret message so that the LSB values carry the hidden message directly. As more and more techniques of hiding information (Steganography) are developed, the methods of detecting the use of steganography (Steganalysis), also advance. Most steganography techniques change the properties of the cover source which increases the probability of detecting the changes. The DCT-M3 algorithm reduces significantly the number of changes in the cover image; the embedding capacity has been improved by 16.7% approximately while maintaining minimum detectability against blind steganalysis schemes. The effectiveness of steganography is measured using three parameters. First, the steganographic technique must provide the maximum information. Second, the embedded data must not be traceable to the viewer. Third, the hidden information should be successfully retrieved at the receiver. It is difficult to recognize the existence of a hidden data in the cover image using the existing methods. This is because embedding is randomly performed in the frequency domain. General TermsSteganography, LSB (Least Significant Bit) image.

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Section: Related Workmentioning

confidence: 75%

An Analysis on LSB Image Steganography with Colour Image as Cover

Chaturvedi¹,

Mohi²

2018

IJCA

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“…In general, there are 2 processes in the steganography method, namely the embedding process which is used to hide messages into the cover-object and extraction processes for message extraction from stego-objects [6] which will later require a secret key so that only authorized users can hide and message extraction [7].…”

Section: Steganographymentioning

confidence: 99%

Upload File Security on the Server Using LSB and Hill Cipher

Handoko

Umam

Anindita

2019

JAIS

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The rapid development of technology not only has a positive impact, but also can have a negative impact such as the development of cyber crime that can cause messages to be unsafe. Message security can be protected using cryptography to convert messages into secret passwords. Steganography is a technique of hiding messages by inserting messages into images that are used to increase message security. In this study, it discusses a combination of hill cipher and LSB algorithms to secure messages. The message used is a 3-bit grayscale image for steganography and text messages with 32, 64 and 128 characters for cryptography. The measuring instruments used in this study are MSE, PSNR, Entropy and travel time (CPU time). Test results prove an increase in security without too damaging the image. This is evidenced by the results of the MSE trial which has a value far below the value 1, the PSNR is> 65 dB with a range of entropy values of 5 to 7, and travel times are almost the same.

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“…Wu et al [24] amalgamated LSB replacement and PVD methodologies to enrich the excellence of stego image. Malathi and Gireeshkumar [25] carried out research to incorporate a large payload with various techniques related to LSB and F5 algorithm. This paper thus included both the spatial (LSB techniques) and transform domains (PVD and F5) for steganalysis.…”

Section: Related Workmentioning

confidence: 99%

Blind Steganalysis for JPEG Images using SVM and SVM-PSO Classifiers

2019

IJITEE

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Blind steganalysis or the universal steganalysis helps to identify hidden information without previous knowledge of the content or the embedding technique. The Support Vector Machine (SVM) and SVM- Particle Swarm Optimization (SVM-PSO) classifiers are adopted for the proposed blind steganalysis. The important features of the JPEG images are extracted using Discrete Cosine Transform (DCT). The kernel functions used for the classifiers in the proposed work are the linear, epanechnikov, multi-quadratic, radial, ANOVA and polynomial. The proposed work uses linear, shuffle, stratified and automatic sampling techniques. The proposed work employs four techniques for image embedding namely, Least Significant Bit (LSB) Matching, LSB replacement, Pixel Value Differencing (PVD) and F5 and applies 25% embedding. The data to the classifier is split as 80:20 for training and testing and 10-fold cross validation is carried out.

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Relating the Embedding Efficiency of LSB Steganography Techniques in Spatial and Transform Domains

Cited by 34 publications

References 7 publications

An Analysis on LSB Image Steganography with Colour Image as Cover

An Analysis on LSB Image Steganography with Colour Image as Cover

Upload File Security on the Server Using LSB and Hill Cipher

Blind Steganalysis for JPEG Images using SVM and SVM-PSO Classifiers

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