Image adaptive high volume data hiding based on scalar quantization

Jacobsen, N.; Solanki, Kaushal; Madhow, Upamanyu; Manjunath, B.S.; Chandrasekaran, S.

doi:10.1109/milcom.2002.1180477

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…In recent works [5][6] [7], it has been shown that digital data can be effectively hidden in an image so as to satisfy the criteria that the degradation to the host image is imperceptible and it should be possible to recover the hidden under a variety of attack. The main idea is to view the data hiding problem as a communication with channel side information [8] [9].…”

Section: Related Workmentioning

confidence: 99%

Steganography: Applying and Evaluating Two Algorithms for Embedding Audio Data in an Image

ElSayed¹

2015

ijacsa

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Abstract-Information transmission is increasing with grow of using WEB. So, information security has become very important. Security of data and information is the major task for scientists and political and military people. One of the most secure methods is embedding data (steganography) in different media like text, audio, digital images. This paper presents two experiments in steganography of digital audio data file. It applies empirically, two algorithms in steganography in images through random insertion of digital audio data using bytes and pixels in image files. Finally, it evaluates both experiments, in order to enhance security of transmitted data.

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Section: Related Workmentioning

confidence: 99%

Steganography: Applying and Evaluating Two Algorithms for Embedding Audio Data in an Image

ElSayed¹

2015

ijacsa

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“…This model is commonly used in steganography [6]. For steganography schemes that hide data in the DCT or wavelet domain (see, for example, [2]), this is a good model as these transforms are known to significantly decorrelate the data. Since the host samples are assumed to be i.i.d., without loss of generality, we assume the data to be one-dimensional.…”

Section: B Outline Of Papermentioning

confidence: 99%

“…3.1], we know that an optimal (in the above asymptotic sense) test declares data to be hidden whenever (2) Thus, the decision statistic simply computes the K-L distance of the empirical PMF from the set of PMFs corresponding to the feasible PMFs after hiding at rate (see Fig. 1).…”

Section: Givenmentioning

confidence: 99%

Detection of Hiding in the Least Significant Bit

Dabeer

Sullivan

Madhow

et al. 2004

IEEE Trans. Signal Process.

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In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF) method that has certain asymptotic optimality properties and methods that are based on knowledge or estimation of the host PMF and, hence, an appropriate likelihood ratio (LR). b) For a known host PMF, it is shown that the composite hypothesis testing problem corresponding to an unknown hiding rate reduces to a worst-case simple hypothesis testing problem. c) Using the results for a known host PMF, practical tests based on the estimation of the host PMF are obtained. These are shown to be superior to the state of the art in terms of receiver operating characteristics as well as self-calibration across different host images. Estimators for the hiding rate are also developed.Index Terms-Approximate log-liklihood ratio test, hypothesis testing, LSB hiding, steganalysis, universal asymptotic optimality.

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“…An elegant solution based on erasures and errors correcting codes is provided to deal with the synchronization problem caused by the use of local adaptive criteria. This framework was first employed in our previous work on high volume data hiding ( [9], [10]), in which a local adaptive criteria was used to preserve the perceptual quality of the hidden image.…”

Section: Coding Framework For Synchronizationmentioning

confidence: 99%

Estimating and undoing rotation for print-scan resilient data hiding

Solanki

Madhow

Manjunath

et al.

2004 International Conference on Image Processing, 2004. ICIP '04.

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This paper proposes a method to hide information into images that achieves robustness against printing and scanning with blind decoding. A significant contribution of this paper is a technique to estimate and undo rotation. The method is based on the fact that laser printers use an ordered digital halftoning algorithm for printing. Using the proposed hiding method, several hundred information bits can be embedded into 512×512 images with perfect recovery against the print-scan operation. Moreover, the hidden images also survive other attacks such as Gaussian or median filtering, scaling or aspect ratio change, heavy JPEG compression, and rows and/or columns removal.

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Image adaptive high volume data hiding based on scalar quantization

Cited by 8 publications

References 8 publications

Steganography: Applying and Evaluating Two Algorithms for Embedding Audio Data in an Image

Steganography: Applying and Evaluating Two Algorithms for Embedding Audio Data in an Image

Detection of Hiding in the Least Significant Bit

Estimating and undoing rotation for print-scan resilient data hiding

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