Artificial Intelligence - Based Steganography Model for Social Media Data Set

Title

Artificial Intelligence - Based Steganography Model for Social Media Data Set

Creator

R, Gurunath

Contributor

Samanta, Debabrata

Description

Steganography, one of the data security mechanisms under our investigation, shields legitimate messages from hackers and spies by employing data hiding. Data protection is newlinecurrently the top priority due to the signifcant advancements in information technology due to high-security concerns. Traditional techniques for maintaining data confdentiality include steganography and cryptography; the distinction is that steganography does not naturally arouse suspicion, whereas cryptography does. Traditional linguistic steganographic methods suffer from limitations in automation, accuracy, and the volume of concealed text. The robustness and undetectability properties of these approaches also require improvement. Third-party vulnerability is often too high for conventional techniques to handle. Artifcial intelligence is increasingly replacing traditional model creation in steganography. Despite the fact that steganography ensures security, information sent over online social networks (OSN) is plainly not safe. Steganography along newlinewith encryption can make a difference with regard to privacy of information in transit. newlineThe research study aims to build algorithms or models and assess steganography s robustness, security, undetectability, and embedding ability. Two distinct types of data newlineconcealing employed for investigation: text and image. The results were encouraging newlinewhen we initially tested our Laplacian model using image steganography and compared newlinewith benchmark methods. The second experiment, which is based on AI, generates the cover text using secret information, examines the security and robustness of steganography. The study compared suggested text steganography model, 3-bit data concealing, with other existing techniques in order to ascertain the undetectability factor. The frst experiment used MATLAB tools, and the second used the markovify python module, RNN (Recurrent Neural networks), and the Huffman tree. Further format-based steganography methods utilized in the following experiment.

Source

Author's Submission

Date

2023-01-01

Publisher

Christ(Deemed to be University)

Subject

Computer Science

Rights

Open Access

Relation

61000275

Format

PDF

Language

English

Type

PhD

Identifier

http://hdl.handle.net/10603/533980