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Quantifying Intermembrane Distances with Serial Image Dilations

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Published on: September 28, 2018

Capacity estimates for data hiding in compressed images.

M Ramkumar¹, A N Akansu

¹IDT Corp., Newark, NJ 07102, USA. ramkumar@corp.idt.net

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 8, 2008

Summary

This study reveals that embedding data in the magnitude Discrete Fourier Transform (DFT) domain significantly boosts image data-hiding capacity. This information-theoretic approach optimizes digital image security and data embedding.

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Area of Science:

Digital Image Processing
Information Theory
Data Security

Background:

Data-hiding in images is crucial for digital security.
Transform domains are commonly used for embedding, but the optimal transform is unclear.
Existing methods often use Discrete Cosine Transform (DCT) or wavelets.

Purpose of the Study:

To estimate the maximum number of bits that can be hidden in still images (data-hiding channel capacity).
To investigate the impact of embedding data in transform domains versus the spatial domain.
To compare the data-hiding capacities of various transform domains.

Main Methods:

An information-theoretic approach was used to estimate channel capacity.
Data embedding was performed in different transform domains, including DCT, Discrete Fourier Transform (DFT), Hadamard, and subband transforms.
Comparisons focused on achievable data-hiding capacities for each transform.

Main Results:

Embedding data in a suitable transform domain significantly increases channel capacity compared to the spatial domain.
The magnitude DFT decomposition demonstrated the highest data-hiding capacity among the tested transforms.
The choice of transform domain critically affects the achievable data-hiding capacity.

Conclusions:

The magnitude DFT offers superior performance for data-hiding channel capacity estimation.
Optimizing the transform domain is essential for maximizing data embedding in images.
This research provides a framework for selecting optimal transforms for secure data-hiding.