Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Steganalysis: Using the blind deconvolution to retrieve the hidden data(2011) Jidesh, P.; George, S.Steganography has gained a substantial attention due to its application in wide areas. Steganography as it literally mean is hiding the information (stego data) inside the data (communication data) so that the receiver can only extract the desired information from the data. Steganalysis is the reverse process of steganography in which the information about the original data is hardly available, from the received data the extractor needs to identify the original data. Since this belong to a class of inverse problems it is hard to find the approximate match of the original data from the received one. In most of the cases this will fall under the category of ill-posed problems. The stego-data that has been embedded into the communication data can be considered as linear bounded operator operating on the input data and the reverse process (the Steganalysis) can be thought like a deconvolution problem by which we can extract the original data. Here we are assuming the watermarking as a linear operation with a bounded linear operator K : X→Y where X and Y are spaces of Bounded Variation (BV). The forward problem (the Steganography) is a direct convolution and the reverse (backward) problem (steganalysis) is a de-convolution procedure. In this work we are embedding a Gaussian random variable array with zero mean and with a specific variance into the data and we show how the original data can be extracted using the regularization method. The results are shown to substantiate the ability of the method to perform steganalysis. © 2011 IEEE.Item New Data Hiding Technique in Encrypted Image: DKL Algorithm (Differing Key Length)(Elsevier, 2015) Udhayavene, S.; Dev, A.T.; Chandrasekaran, K.This paper introduces a new technique to increase the information security over the network using steganography in such a way that the secret message being sent is unidentifiable. There is a comparison made to give a clear view of how the algorithm proposed is better than LSB algorithm which is used since a long time for sending concealed messages. To avoid the chances of an attacker using steganalysis to retrieve the data, the data encryption is done. S-tool is used to show the reliability of this algorithm. We will be comparing both LSB and DKL algorithms on the basis of Mean Square Error, Peak Signal Noise Ratio, Relative Payload and Rate of Embedding. Here by its shown that DKL algorithm is more efficient than LSB algorithm. © 2015 The Authors.Item Dual stage text steganography using unicode homoglyphs(Springer Verlag service@springer.de, 2015) Hosmani, S.; Bhat, H.G.R.; Chandrasekaran, K.Text steganography is hiding text in text. A hidden text gets hidden in a cover text to produce a plain looking stego text. This plain looking stego text is posted as the message which no one suspects to contain anything concealed. Today, text messages are a common mode of communication over the internet and it is associated with a huge amount of traffic. Steganography is an added layer of protection that can be used for security and privacy. In this paper, we describe a text steganography approach that provides a good capacity and maintains a high difficulty of decryption. We make use of approaches of space manipulation, linguistic translation and Unicode homoglyphs in our algorithm. Our implementation is in Python. Also, we explain a parallel approach for hiding large hidden text messages in large cover text messages. © Springer International Publishing Switzerland 2015.Item Multilevel Security Framework with Fault Tolerance using Secret Image Sharing and Steganography(Institute of Electrical and Electronics Engineers Inc., 2024) Gound, Y.S.; Purushothama, B.R.Secure data embedding within digital images has become crucial for protecting sensitive information against unauthorized access. In this work, we propose a multilevel security that integrates data embedding, two-level secret sharing, and message extraction within digital images. The embedding process involves encoding data into grayscale images using random least significant bit substitution, ensuring covert integration while preserving image quality. Subsequently, a multilevel secret sharing scheme based on Shamir’s secret sharing is applied to generate multiple shares of the image, enhancing security through distributed storage and threshold-based reconstruction. The extraction phase employs polynomial interpolation to reconstruct the original image from the shares, facilitating seamless retrieval of embedded data. The adversary will not be able to obtain any information about the secret without significant computation cost. © 2024 IEEE.