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Author

Uttiya Ghosh

Bio: Uttiya Ghosh is an academic researcher from St. Thomas' College of Engineering and Technology. The author has contributed to research in topics: Data security & Steganography. The author has an hindex of 1, co-authored 1 publications receiving 3 citations.

Papers
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Book ChapterDOI
01 Jan 2018
TL;DR: A new technique has been introduced where various parameters determine the number of bits embedded and it is shown empirically that this method withstands the statistical attacks and benchmark.
Abstract: With the increase of communication over Internet, the issue of security has become an important factor. Steganography is a consequence of the increasing degradation of reliability. The roots of steganography lie in ancient Greek civilization. With time, steganography has moved a long way in the path of advancement. It started off with least significant bit (LSB) embedding which mainly focussed on data security. With time, many algorithms have been designed using multi-bit steganography which takes into account both security and capacity of data embedded. In this paper, a new technique has been introduced where various parameters determine the number of bits embedded. This helps to improve the robustness of this method. We show empirically that our method withstands the statistical attacks and benchmark.

4 citations


Cited by
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Journal ArticleDOI
TL;DR: A thorough review of existing types of image steganography and the recent contributions in each category in multiple modalities including general operation, requirements, different aspects, different types and their performance evaluations is provided.

253 citations

Journal ArticleDOI
TL;DR: The objective of the paper is to examine and scrutinize the different algorithms of steganography based on parameters like PSNR, MSE, and Robustness and make recommendations for producing high-quality stego images, high-payload capacity, and robust techniques of Steganography.
Abstract: The amount of data exchanged via the Internet is increasing nowadays. Hence, data security is termed as a serious issue while communication of data is processed over the Internet. Everyone needs th...

40 citations

Proceedings ArticleDOI
23 Mar 2022
TL;DR: In this paper , a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional GANs (DCGAN).
Abstract: The success of deep learning based steganography has shifted focus of researchers from traditional steganography approaches to deep learning based steganography. Various deep steganographic models have been developed for improved security, capacity and invisibility. In this work a multi-data deep learning steganography model has been developed using a well known deep learning model called Generative Adversarial Networks (GAN) more specifically using deep convolutional Generative Adversarial Networks (DCGAN). The model is capable of hiding two different messages, meant for two different receivers, inside a single cover image. The proposed model consists of four networks namely Generator, Steganalyzer Extractor1 and Extractor2 network. The Generator hides two secret messages inside one cover image which are extracted using two different extractors. The Steganalyzer network differentiates between the cover and stego images generated by the generator network. The experiment has been carried out on CelebA dataset. Two commonly used distortion metrics Peak signal-to-Noise ratio (PSNR) and Structural Similarity Index Metric (SSIM) are used for measuring the distortion in the stego image The results of experimentation show that the stego images generated have good imperceptibility and high extraction rates.

1 citations