A steganographic method based upon JPEG and quantization table modification

Chang, Chin‐Chen; Chen, Tung-Shou; Chung, Lou-Zo

doi:10.1016/s0020-0255(01)00194-3

Cited by 274 publications

(195 citation statements)

References 5 publications

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“…The new version of the QT gives them 36 coefficients in each 8x8 block to embed their secret data into which yields a reasonable payload. Their work was motivated by a prior published work [40]. Steganography based on DCT JPEG compression goes through different steps as shown in Fig.16.…”

Section: Steganography In the Image Frequency Domainmentioning

confidence: 99%

Digital image steganography: Survey and analysis of current methods

et al. 2010

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Steganography is the science that involves communicating secret data in an appropriate multimedia carrier, e.g., image, audio, and video files. It comes under the assumption that if the feature is visible, the point of attack is evident, thus the goal here is always to conceal the very existence of the embedded data. Steganography has various useful applications. However, like any other science it can be used for ill intentions. It has been propelled to the forefront of current security techniques by the remarkable growth in computational power, the increase in security awareness by, e.g., individuals, groups, agencies, government and through intellectual pursuit. Steganography's ultimate objectives, which are undetectability, robustness (resistance to various image processing methods and compression) and capacity of the hidden data, are the main factors that separate it from related techniques such as watermarking and cryptography. This paper provides a state-of-the-art review and analysis of the different existing methods of steganography along with some common standards and guidelines drawn from the literature. This paper concludes with some recommendations and advocates for the object-oriented embedding mechanism. Steganalysis, which is the science of attacking steganography, is not the focus of this survey but nonetheless will be briefly discussed.

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Section: Steganography In the Image Frequency Domainmentioning

confidence: 99%

Digital image steganography: Survey and analysis of current methods

et al. 2010

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“…VQ compression method [3] is a powerful and simple image compression method. However, the performance of compression rate can be further improved.…”

Section: Vq Compressionmentioning

confidence: 99%

“…Vector quantization (VQ) [3]approach is spatial domain image compression methods. VQ is a lossy compression method that uses codeword indices to represent images, where the code words are taken from a common codebook that contains the proper number of code words.…”

Section: Iintroductionmentioning

confidence: 99%

“…When an image is to be compressed by the VQ system, the image is first partitioned into non-overlapping blocks and each block is mapped to the closest codeword in the codebook. The SMVQ [3] method predicts an input block by using the adjacent values from its upper and left blocks to form a temporary codeword. The temporary codeword is used to select a part of code words from the main codebook to form a state codebook.…”

Section: Iintroductionmentioning

confidence: 99%

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A Survey on Data Hiding and Compression Schemes

Ramya¹,

K.Kathiresan²,

Kalaiarasi³

2014

IJAREEIE

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Data hiding has a vital role to play in information security. Using a data hiding technique, secret information is hidden into cover digital content. Compression techniques and data hiding techniques received much less attention from the research community and from industry than cryptography. In past research many data hiding and image compression techniques has been developed, it works independent module in both sender and server side. These method cause low efficiency, less security and also the low bit rate scheme requires more time to encode. Both image compression and data hiding (EJDHC) using residual codebooks with side match vector quantization (SMVQ). In this survey is to discuss on data hiding schemas and compression techniques such as JPEG, JPEG 2000, vector quantization, VQ compression and SMVQ. KEYWORDS:Data hiding, image compression, side match vector quantization I.INTRODUCTIONData hiding is a process to hide data into cover media. The data hiding process links two sets of data and a set of the embedded data and another set of the cover media data. The relationship between these two sets of data characterizes different applications. In authentication phase embedded data are closely related to the cover media. High Data hiding in images [1] 8-bit grayscale images are selected as the cover media called as cover images. Cover images with the secret messages embedded in the images. For data hiding methods, the image quality refers to the quality of the images. Most of the hiding techniques is based on manipulating the least-signi7cant-bit (LSB) planes by directly replacing the LSBs of the cover image with the message bits. LSB methods typically achieve high capacity. Another technique introduced in [2] to improve image quality a local pixel adjustment process an optimal substitution matrix for the embedding of the secret messages. Image compression purpose is data reduction that can reduce the quantity of data. Image compression resolved the problem of reducing the amount of data required to represent an image. It is a process planned to yield a compact representation of an image by reducing the image storage. In Image compression has two ways lossy and lossless schemes Lossy schemes provide much higher compression ratios than lossless schemes. Lossy schemes are widely used since the quality of the reconstructed images is adequate for most applications. Lossless compression is used for medical imaging and arts. Data compression is being used for applications in image processing. In fact, very surprising we can see that many authors use data compression techniques for classification and / or segmentation images, filter or denoising image, artifacts detection in images, detecting altered images, etc.Data hiding [6] also can be classified into three domains, namely, spatial, transformative, and compression. In the spatial domain each pixel in the cover image is modified to hide the secret information. In the transformative domain the cover image is transformed into coefficients using well-known...

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“…Taking these aspects into consideration working in frequency domain becomes more attractive. Here, sender transforms the cover image into frequency domain coefficients before embedding secret messages in it [7]. Different sub-bands of frequency domain coefficients give significant information about where vital and non vital pixels of image resides.…”

Section: B Transform Domain Based Steganographymentioning

confidence: 99%

A Secure Skin Tone based Steganography Using Wavelet Transform

Shejul¹,

Kulkarni²

2011

IJCTE

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Abstract-Steganography is the art of hiding the existence of data in another transmission medium to achieve secret communication. Steganography method used in this paper is based on biometrics. And the biometric feature used to implement Steganography is skin tone region of images [1]. Here secret data is embedded within skin region of image that will provide an excellent secure location for data hiding. For this skin tone detection is performed using HSV (Hue, Saturation and Value) color space. Additionally secret data embedding is performed using frequency domain approach -DWT (Discrete Wavelet Transform), DWT outperforms than DCT (Discrete Cosine Transform). Secret data is hidden in one of the high frequency sub-band of DWT by tracing skin pixels in that sub-band. For data hiding two cases are considered, first is with cropping and other is without cropping. In both the cases different steps of data hiding are applied either by cropping an image interactively or without cropping i.e. on whole image. Both cases are compared and analyzed from different aspects. This is concluded that both cases offer enough security. Main feature of with cropping case is that this results into an enhanced security because cropped region works as a key at decoding side. Where as without cropping case uses embedding algorithm that preserves histogram of DWT coefficient after data embedding also by preventing histogram based attacks and leading to a more security. This study shows that by adopting an object oriented steganography mechanism, in the sense that, we track skin tone objects in image, we get a higher security. And simulation result shows that satisfactory PSNR (Peak-Signal-to-Noise Ratio) is also obtained.

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A steganographic method based upon JPEG and quantization table modification

Cited by 274 publications

References 5 publications

Digital image steganography: Survey and analysis of current methods

Digital image steganography: Survey and analysis of current methods

A Survey on Data Hiding and Compression Schemes

A Secure Skin Tone based Steganography Using Wavelet Transform

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