Text Hiding by Anime Character Generation

(Encryption Decryption Framework)

Overview:

The privacy of secret information has gotten a lot of attention in the last few decades due to the newly developed hacking techniques and malware attacks. The image steganography played an important role in securely transmitting the secret information to the recipient. Image steganography is a process of integrating secret information into the carrier image by hiding the existence of secret information. Image steganography showed significant advancements in recent years after the rapid growth of Generative adversarial Networks (GAN). The majority of the work has been done by using the natural image as carrier images to hide secret information. The aim of the proposed work is to find the solution of text hiding, where the more secure and robust framework will generate the Stego image with increased payload capacity. The proposed framework will hide the text data by directly generating the anime characters without using a natural image (cover image). The anime characters dataset and news dataset will be used for the generation of Anime. Transfer learning of pre-trained GAN and CNN models will also be applied for the encryption and decryption of text data into anime characters.

Problem Statement

An encryption decryption-based text hiding framework to encrypt and decrypt text information with increased security and payload capacity. The framework will encrypt text by generating anime faces using GAN and decrypt anime faces by illustration module to extract text information.

Research Challenges/Gaps

Research Gap

Beneficiaries of the Project

Motivation

Objectives

Related Datasets

Proposed Architecture

Architecture of Proposed Framework

Stego Image Indexing

Indexing Algorithm

▸Generate a Grid of 8*8 using Secret Key.

▸Place Generated Stego image in Grid according to the position.

▸Place Random Images at 0 Indexes.

Results

Comparison of Results

Hiding Capacity Evaluation


Robustness Evaluation

Clarity Evaluation

Security Evaluation


Team

Supervisor

MS Scholar

Dr. Usama Ijaz Bajwa 

Hafiz Abdul Rehman

usamabajwa@cuilahore.edu.pk

Co-PI, Video Analytics lab, National Centre in Big Data and Cloud Computing,

Program Chair (FIT 2019),

HEC Approved PhD Supervisor,

Associate Professor & Associate Head of Department

Department of Computer Science,

COMSATS University Islamabad, Lahore Campus, Pakistan

www.usamaijaz.com

www.fit.edu.pk

Job Profile

Google Scholar Profile

abdulrehman19427@gmail.com

Research Scholar (RCS),

Department of Computer Science,

COMSATS University Islamabad, Lahore Campus, Pakistan

Google Scholar Profile

Research Gate Profile