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 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. = Modulated Message - ScaledCover ImageStego-Image = De-noised ImageExtracted Noise