VRL an eye

Image Adaptive Data Hiding

People

K. Solanki, A. Sarkar, N. Jacobsen, U. Madhow, B.S. Manjunath, S. Chandrasekaran

Objective

The goal of data hiding is embed as much information as possible into a host without causing any perceptual distortion, and still be robust enough to survive benign or malicious attacks.

In other words, there are three conflicting requirements for a data hiding scheme:

  1. Transparency: The host signal should not undergo any perceptual degradation.
  2. Robustness: The embedded signal must survive benign and malicious attacks.
  3. Capacity: Embed as much information as possible into the host.

Our objective is to hide large volumes of data in images, in a manner that causes minimal perceptual distortion, and is robust to "benign" JPEG compression as well as more malicious additive white Gaussian noise (AWGN) attacks.

Demo

» Data Hiding Demonstration

Image-Adaptive Data Hiding

We have designed a high-volume data hiding scheme that uses image-adaptive criteria and powerful error correcting codes, and is robust against a variety of attacks such as JPEG compression, AWGN, wavelet-based compression (JPEG 2000), image resizing, and image tampering. It has the following main ingredients.

Examples

Selective Embedding in Coefficients (SEC) Scheme

Entropy Thresholding (ET) Scheme

Three test images are presented here with data hidden at design quality factors (QF) of 75, 50, and 25 respectively along with number of bits embedded. It should be noted that lower design quality factors indicate more robustness against attacks.

» Robustness against attacks

Tamper Detection and Localization

This coding framework can also deal with image tampering wherein a part of image is replaced by some other image data. Such a tampering can be local or global. In order to survive tampering, the code rate used is further lowered so that we can deal with the errors caused due to the replacement of the image data. Once the hidden bitstream is decoded, localization of the tampered area can be done by finding the locations in the host image where errors have occurred. If the host image has undergone tampering, then most of the errors would be concentrated at the locations where the tampering was done. Such an ability to robustly decode the embedded bitstream and then localize the tampered area can be useful in medical or forensic applications to detect whether a malicious attacker has tampered with the "evidence".

Examples

Acknowledgements

This research is supported by a grant from ONR #N00014-01-1-0380. Program manager: Dr. Ralph Wachter

Publications

    2010

  1. L. Nataraj, A. Sarkar and B. S. Manjunath,
    "Precise Localization Of Key-points To Identify Local Regions For Robust Data Hiding"
    IEEE International Conference on Image Processing, Sep. 2010.
    [abstract] [PDF] [BibTex]

    Abstract preview: "We propose a novel data hiding system where data is embedded in local non-overlapping regions in an image. To survive cropping, the encoder embeds the same data in multiple regions of fixed dimensions..." [more]

  2. Anindya Sarkar, Kaushal Solanki, and B. S. Manjunath,
    "Obtaining Higher Rates for Steganographic Schemes while Maintaining the Same Detectability"
    Information Hiding Workshop 2010, Calgary, Canada, Jun. 2010.
    [abstract] [PDF] [BibTex]

    Abstract preview: "This paper focuses on modifying the decoder module for an active steganographic scheme to increase the effective data-rate with- out affecting the embedding module. Three techniques are suggested to i..." [more]

  3. 2009

  4. A. Sarkar and B. S. Manjunath,
    "Double Embedding in the Quantization Index Modulation Framework"
    International Conference on Image Processing, 2009, Cairo, Egypt, pp. 3653 - 3656, Nov. 2009.
    [abstract] [PDF] [BibTex]

    Abstract preview: "In this paper, we perform double embedding in the quantization index modulation (QIM) framework where a single coefficient is modified twice, using two quantizers, to embed two bits. The motivation be..." [more]

  5. Anindya Sarkar and B. S. Manjunath,
    "Image Dependent Log-likelihood Ratio Allocation for Repeat Accumulate Code based Decoding in Data Hiding Channels"
    Technical Report, VRL, ECE, University of California, Santa Barbara, Sep. 2009.
    [abstract] [PDF] [BibTex]

    Abstract preview: "Error correction codes of suitable redundancy are used for ensuring perfect data recovery in noisy channels. For iterative decoding based methods, the decoder needs to be initialized with proper confi..." [more]

  6. Anindya Sarkar, Lakshmanan Nataraj and B. S. Manjunath,
    "Robust Key-point based Data Hiding"
    Technical Report, VRL, ECE, University of California, Santa Barbara, Sep. 2009.
    [abstract] [PDF] [BibTex]

    Abstract preview: "We propose a robust data-hiding method which can survive a host of global, geometric and image editing attacks. Localized redundant embedding around multiple key-point centered regions are used to pro..." [more]

  7. 2007

  8. A. Sarkar, U. Madhow, S. Chandrasekaran and B. S. Manjunath,
    "Adaptive MPEG-2 Video Data Hiding Scheme"
    Proc. SPIE Security, Steganography, and Watermarking of Multimedia Contents IX, Jan. 2007.
    [abstract] [PDF] [BibTex]

    Abstract preview: "We have investigated adaptive mechanisms for high-volume transform-domain data hiding in MPEG-2 video which can be tuned to sustain varying levels of compression attacks. The data is hidden in the unc..." [more]

  9. 2004

  10. K. Solanki, N. Jacobsen, U. Madhow, B. S. Manjunath and S. Chandrasekaran,
    "Robust Image-Adaptive Data Hiding Based on Erasure and Error Correction"
    IEEE Transactions on Image Processing, vol. 13, no. 12, pp. 1627-1639, Dec. 2004.
    VRL ID: 141, [abstract] [PDF] [BibTex]

    Abstract preview: "Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we propose practical realizations of this prescription f..." [more]

  11. 2003

  12. K. Solanki, O. Dabeer, U. Madhow, B. S. Manjunath and Shiv Chandrasekaran,
    "Robust Image-Adaptive Data Hiding: Modeling, Source Coding, and Channel Coding"
    41st Allerton Conference on Communications, Control, and Computing, Oct. 2003.
    VRL ID: 125, [abstract] [PDF] [BibTex]

    Abstract preview: "This paper provides a summary of our work over the past two years on robust, high-volume data hiding in images. We first present a basic framework for image-adaptive hiding, which allows selection of ..." [more]

  13. 2002

  14. N. Jacobsen, K. Solanki, U. Madhow, B. S. Manjunath and S. Chandrasekaran,
    "Image-adaptive hidgh-volume data hiding based on scalar quantization"
    Proc. IEEE Military Communications Conference (MILCOM), Anaheim, CA, USA, vol. 1, pp. 411-415, Oct. 2002.
    VRL ID: 110, [abstract] [PDF] [BibTex]

    Abstract preview: "Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this presc..." [more]

  15. K. Solanki, N. Jacobsen, S. Chandrasekaran, U. Madhow, and B. S. Manjunath,
    "High-volume data hiding in images: Introducing perceptual criteria into quantization based embedding"
    Proc. ICASSP, vol. 4, pp. 3485-3488, May. 2002.
    VRL ID: 96, [abstract] [PDF] [BibTex]

    Abstract preview: "Information-theoretic analyses for data hiding prescribe em-bedding the hidden data in the choice of quantizer for the host data. In this paper, we consider a suboptimal implementation of this prescri..." [more]