An Adaptive Image Steganography Method Based on Histogram of Oriented Gradient and PVD-LSB Techniques

Project Code :TMMAIP367

Objective

The main objective of the project is to provide security for the images using histogram of oriented techniques.

Abstract

In this work, encryption & decryption process will take place based on Histogram of Oriented Gradient (HOG) and Pixel Value Differencing (PVD)-Least Significant Bit (LSB) substitution. Steganography is the practice of hiding a secret message inside of something that is no secret. 

Due to the increase and urgent demand for data transmission through social networks, information hiding was one of the most critical concerns in human societies. The common use of the Internet and cloud providers in data storage over open networks and unreliable platforms expose private and confidential data to dangerous circumstances. A number of data hiding schemes have been developed, including steganography, in order to keep an unauthorized individual from transmitting the information. 

Applied to the hiding of sensitive documents on different forms of digital media, such as photographs, audio and video, and email. Here, we embed and extract the text message into images and text messages from the image.

Keywords: Data hiding, Steganography, Pixel value differencing, Least significant bit, Histogram of oriented gradient, HOG.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software: Matlab 2018a or above

Hardware:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support

Disk:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • What is EISPACK & LINPACK
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:

    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    • Morphological processing
    • Segmentation etc.,

  • How embed & extract a message in image
  • How to extend our work to another real time applications
  • Project development Skills

    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

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