1. VISVESV
ARAYATECHNOLOGICALUNIVERSITY
BELAGAVI, KARNATAKA-570014
PROJECT PHASE 1:
“MULTI IMAGE STEGANOGRAPHY”
BHARATH M N
CHANNAKESHAVA B L
SAGAR B K
THARUN J M
4GK20CS005
4GK20CS010
4GK20CS027
4GK20CS038
UNDER THE GUIDANCE OF
Dr. NAVEEN T H
ASSISTANT PROFESSOR
DEPT OF COMPUTER SCIENCE &
ENGG, K K GECK
K R PETE KRISHNA GOVERNMENT ENGINEERING COLLEGE
Department of Computer Science and Engineering
HEAD OF THE DEPARTMENT
Dr.HAREESH K
ASSOCIATE PROFESSOR & HOD
DEPT OF COMPUTER SCIENCE
& ENGG, K K GECK
2. CONTENTS:
• Introduction
• Problem Statement
• Background work
• Approach
• Model Architecture
• Image Processing
• Aim Of The Project
• Literature survey
• Conclusion
• Reference
3. INTRODUCTION:
• The focus of this introduction is on the steganography of images, a widely explored domain within
the broader field of information security.
• Image steganography aims to embed secret information within image files in such a way that the
alteration is imperceptible to the human eye and difficult to detect through conventional means.
• The objective of steganography in multiple images is to distribute the payload of hidden information
across a collection of images, thus leveraging the combined capacity of the ensemble.
• This approach not only increases the overall data hiding capacity but also introduces a level of
redundancy and resilience.
4. EXISTING SYSTEM:
1. LSB Substitution: This method involves replacing the least significant bits of pixel values with
secret data bits. As the changes are minimal, they are often imperceptible to the human eye.
2. Spread Spectrum: It disperses the secret data across the entire image by slightly modifying the
pixel values in a way that doesn’t draw attention but allows for data retrieval.
3. Transform Domain Techniques: Using mathematical transformations like Discrete Cosine
Transform (DCT) or Discrete Fourier Transform (DFT) to embed data in the frequency domain.
This method is commonly used in JPEG steganography
4. Random LSB Embedding: Similar to LSB substitution, but instead of using a fixed pattern, it
embeds data in a randomized manner to make it harder to detect.
5. LITERATURE SURVEY:
NAME YEAR TECHNIQUES
Chin-Ling Chen 29 Jan 2022
Improved image quality
In steganography
Shumeet Baluja 2020
Single image
steganography using
Deep neural network
Felix Kreuk 2019
Deep neural network for
Speech steganography
Gutub and
shaarani
2020
Efficient Implementation of
image Secret Hiding Based on
LSB and DWT Steganography
Comparisons
6. LITERATURE SURVEY:
NAME YEAR TECHNIQUES
YC HU 2006 Multiple images
embedding scheme based
on moment preserving
block truncation codinG
ARUN KUMAR
RAVICHANDRAN K.S
2019 RIWT and QR factorization
based hybrid robust image
steganography using block
selection algorithm for IoT
devices
7. PROBLEM STATEMENT:
• We aim to hide multiple images (2 or more) in one not-so-secret cover
image.
• The embedded secret images must be retrievable with minimum loss.
• The encoded cover image must look like the original cover image.
9. Approach :
• Extend single image steganography to multi image steganography by combining
from both the papers.
• Extend the idea by creating multiple prep network for separate images in the
encoder and joining them with the cover image.
• Using multiple reveal networks or multiple decoders to retrieve separate secrete
messages from the container or encoded images.
• In this Project Tiny ImageNet Dataset is used.
11. IMAGE PROCESSING
• Embedding Techniques:
Spatial Domain Techniques: Image pixels are directly manipulated to embed the secret information.
Common techniques include Least Significant Bit (LSB) substitution, where the least significant bit of each
pixel is replaced with the secret data.
Transform Domain Techniques: Embedding is performed in a transformed version of the image, often
using techniques like discrete cosine transform (DCT) or discrete wavelet transform (DWT).
• Payload Distribution Across Multiple Images:
The secret information is distributed across multiple images in a way that collectively maximizes the data-
hiding capacity. This may involve assigning different portions of the payload to different images within the
ensemble.
12. IMAGE PROCESSING
• Synchronization and Coherence:
Ensuring that the embedded information remains coherent and synchronized across the multiple images is crucial. Image
processing techniques are employed to manage the distribution of payload, maintaining a balance between data hiding
capacity and the visual quality of each image.
• Encryption and Security Measures:
Image processing may involve incorporating encryption techniques to enhance the security of the embedded information.
Encryption ensures that even if an unauthorized entity detects the presence of hidden data, deciphering its meaning
remains a formidable challenge
• Robustness Against Attacks:
Image processing techniques are implemented to enhance the robustness of the steganographic system against various
attacks. This includes resisting attempts to remove or alter the hidden information, as well as withstanding common image
processing operations that might be applied by attackers.
13. AIMS OF THE PROJECT :
• Increased Data Capacity
• Enhanced Security
• Improved Robustness
• Redundancy and Error Recovery
• Resistance to Steganalysis Tools
• Flexibility And Scalability
• Preservation of Visual Quality
• Adaptability to Diverse Environments
14. CONCLUSION:
• Image steganography serves as an effective method for securing sensitive information by
embedding it within the visual content of images. This covert communication allows for the
exchange of data without attracting unwanted attention.
• A fundamental aspect of image steganography is its emphasis on imperceptibility. Successful
steganographic techniques ensure that the visual integrity of the carrier image is maintained,
making it challenging for observers to detect the presence of hidden data with the naked eye.
• Image steganography finds applications across various domains, including secure
communication, data protection, digital watermarking, and copyright protection. Its versatility
makes it a valuable tool in different scenarios where the concealment of information is crucial.
15. REFERENCE:
• Baluja S. 2019: Hiding images within images. IEEE Transactions on Pattern Analysis
and Machine Intelligence 42(7):1685-169
• Elzeki OM, Elfattah MA, Salem H, Hassanien AE, Shams M. 2021: A novel
perceptual two layer image fusion using deep learning for imbalanced COVID-19
dataset. PeerJ Computer Science 7:e364
• Hu YC. 2006: Multiple images embedding scheme based on moment preserving
block truncation coding. Fundamenta Informaticae 73(3):373-387
• Hwang HJ, Kim S, Kim HJ. 2016: Reversible data hiding using least square predictor
via the LASSO. EURASIP Journal on Image and Video Processing 2016(1):42
16. REFERENCE:
• Al-Shaarani F, Gutub (2021)a: Increasing participants using counting-based secret sharing via
involving matrices and practical steganography. Arabian Journal for Science and
Engineering In Press
• Al-Shaarani F, Gutub(2021)b: Securing matrix counting-based secret-sharing involving crypto
steganography. Journal of King Saud University-Computer and Information Sciences In Press
• Arunkumar S, Subramaniyaswamy V, Ravichandran KS, Logesh R. 2019a: RIWT and QR
factorization based hybrid robust image steganography using block selection algorithm for
IoT devices. Journal of Intelligent & Fuzzy Systems 35(5):4265-4276
• Arunkumar S, Subramaniyaswamy V, Vijayakumar V, Chilamkurti N, Logesh R. 2019b. SVD-
based robust image steganographic scheme using RIWT and DCT for secure transmission of
medical images. Measurement 139:426-437
