Johann A. Briffa Mahesh Theru Manohar Das A Robust Method For Imperceptible High- Capacity...

Johann A. Briffa Mahesh Theru Manohar Das A Robust Method For Imperceptible High-Capacity Information Hiding in Images. INTRODUCTION The art of Hidden Communication dates back to the beginning of western history. Two commonly used techniques are: Cryptography and Steganography. Cryptography is the art of using codes and ciphers to make the message unreadable. The message is visible, but needs to be deciphered. Example: kvofw Steganography is the art and science of conveying information in such a way that its presence is unnoticed . The message is made invisible. Example: via the old lemon trick. Applications of Steganography include Covert Communication, Security, and Data Augmentation. Imperceptibility is important for all these applications. One of the key issues in Steganography is the volume of the message that can be hidden. As in any communication system, there is a trade-off between data rate (i.e., message length) and channel noise. A Robust method has to be developed for Imperceptible High Capacity Information Hiding. RESEARCH OBJECTIVE The goal of this research is to advance the state-of-the-art of high capacity information hiding in images, with particular application to covert communication (Steganography). This is achieved by developing: Novel channel models for image steganography; Devising methods for bandwidth-efficient signal embedding, extraction and error protection; Ensuring visual and statistical imperceptibility. Message Encoding Scheme The following message encoding and decoding schemes have been implemented to date. Each pixel in the Hidden Message embeds one raw bit. The raw message bits are modulated by generating a uniform random number ui in [0,1) for every pixel position. To embed a Zero, we use ui and to embed a One, we transform it using: ui + 0.5, 0.0 ≤ ui ≤ 0.5 g(ui) = ui - 0.5, 0.5 ≤ ui ≤ 1.0 The result is transformed into a Gaussian distribution using the Inverse Gaussian Cumulative Distribution Function. Finally this is scaled, interleaved, and added to the cover image. Block Diagram of Spread Spectrum Image Steganography System Spread Spectrum Image Steganography Encoding Cover Image Message Encryption Low-Rate ECC Modulation Pseudo-Random Noise Generator Interleaver Quantizer Stegoimage Received Stegoimage Message ECC Decoder Restoration Filter Demodulation Pseudo-Random Noise Generator Deinterleaver Decryption Spread Spectrum Image Steganography Decoding Message Decoding Scheme At the receiving end, first the embedded noise is estimated by subtracting a denoised version of the stego–image . To demodulate: The Gaussian noise estimate is first normalized; Next, it is transformed by the Gaussian cumulative distribution function into a uniform sequence; For each pixel position, the estimated value is compared to ui and g(ui), yielding the relative probability that a 0 or a 1 is represented. After decoding the forward error correcting code and after decrypting / decompressing the data stream, an estimate of the embedded message is available. ON-GOING WORK Our on-going research is focused on development of Blind Detection methods for presence of hidden information in Images. Cover Image Message Encryption Low-Rate ECC Modulation Pseudo-Random Noise Generator Interleaver Quantizer Stegoimage = Received Stegoimage Message ECC Decoder Restoration Filter Demodulation Pseudo-Random Noise Generator Deinterleaver Decryption Modulated Message - Scaled Cover Image Stego-Image = Stego-Image De-noised Image Extracted Noise

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Transcript of Johann A. Briffa Mahesh Theru Manohar Das A Robust Method For Imperceptible High- Capacity...

Johann A. Briffa Mahesh TheruManohar Das

A Robust Method For Imperceptible High-Capacity Information Hiding in Images.

INTRODUCTION

The art of Hidden Communication dates back to the beginning of western history. Two commonly used techniques are: Cryptography and Steganography.

Cryptography is the art of using codes and ciphers to make the message unreadable. The message is visible, but needs to be deciphered. Example: kvofw

Steganography is the art and science of conveying information in such a way that its presence is unnoticed . The message is made invisible. Example: via the old lemon trick.

Applications of Steganography include Covert Communication, Security, and Data Augmentation. Imperceptibility is important for all these applications.

One of the key issues in Steganography is the volume of the message that can be hidden. As in any communication system, there is a trade-off between data rate (i.e., message length) and channel noise.

A Robust method has to be developed for Imperceptible High Capacity Information Hiding.

RESEARCH OBJECTIVE

The goal of this research is to advance the state-of-the-art of high capacity information

hiding in images, with particular application to covert communication (Steganography).

This is achieved by developing:

Novel channel models for image steganography;

Devising methods for bandwidth-efficient signal embedding, extraction and error

protection;

Ensuring visual and statistical imperceptibility.

Message Encoding Scheme

The following message encoding and decoding schemes have been implemented to date.

Each pixel in the Hidden Message embeds one raw bit.

The raw message bits are modulated by generating a uniform random number ui in [0,1) for every pixel position.

To embed a Zero, we use ui and to embed a One, we transform it using:

ui + 0.5, 0.0 ≤ ui ≤ 0.5 g(ui) =

ui - 0.5, 0.5 ≤ ui ≤ 1.0

The result is transformed into a Gaussian distribution using the Inverse Gaussian Cumulative Distribution Function.

Finally this is scaled, interleaved, and added to the cover image.

Block Diagram of Spread Spectrum Image Steganography System

Spread Spectrum Image Steganography Encoding

CoverImage

Message EncryptionLow-Rate

ECC

ModulationPseudo-RandomNoise Generator

Interleaver

Quantizer Stegoimage

ReceivedStegoimage

Message

ECC Decoder

RestorationFilter

Demodulation

Pseudo-RandomNoise Generator

Deinterleaver Decryption

Spread Spectrum Image Steganography Decoding

Message Decoding Scheme

At the receiving end, first the embedded noise is estimated by subtracting a denoised version of the stego–image .

To demodulate:

The Gaussian noise estimate is first normalized; Next, it is transformed by the Gaussian cumulative distribution function into a

uniform sequence; For each pixel position, the estimated value is compared to ui and g(ui), yielding

the relative probability that a 0 or a 1 is represented.

After decoding the forward error correcting code and after decrypting / decompressing the data stream, an estimate of the embedded message is available.

ON-GOING WORK

Our on-going research is focused on development of Blind Detection methods for presence

of hidden information in Images.