Detecting LSB steganography in color, and gray-scale images

Fridrich, Jessica; Goljan, Miroslav; Du, Rui

doi:10.1109/93.959097

Cited by 681 publications

(232 citation statements)

References 7 publications

Supporting

Mentioning

229

Contrasting

Unclassified

Order By: Relevance

“…Estimated number of pixels with flipped LSBs for the test image in Fig. 29, with the actual numbers that should be detected in an ideal case (indicated in parenthesis) [112]. Cancelli et al [37] reveal that the performance of current state-of-the-art steganalysis algorithms for detection of 1 steganography is highly sensitive to the used training and testing databases.…”

Section: Fig 29mentioning

confidence: 99%

“…However, inspired by the steganalysis techniques for gray level images, Cheng and Kot [111] successfully created a system able to counter-attack such methods by exploiting the wavelet statistic features extracted from the reconstructed gray level image through the inverse halftoning of a given halftone image fed into the support vector machine's classifier. Jessica and Goljan [112] propose a statistical method that uses higher-order statistics called RS steganalysis; it is designed to provide an estimated percentage of flipped pixels caused by embedding as can be seen from Table 6 ) and (e) the histogram which describes both matrices.…”

Section: Steganalysismentioning

confidence: 99%

See 1 more Smart Citation

Digital image steganography: Survey and analysis of current methods

et al. 2010

View full text Add to dashboard Cite

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.

show abstract

Section: Fig 29mentioning

confidence: 99%

Section: Steganalysismentioning

confidence: 99%

Digital image steganography: Survey and analysis of current methods

et al. 2010

View full text Add to dashboard Cite

show abstract

“…Westfeld [27] designed a steganographic algorithm F 5 that uses matrix coding to minimize the modifications of the LSBs. Fridrich et al [14,15] reported several techniques for detecting steganographic content in images. If a message is inserted into the LSBs of an image, some features of the image change in a manner that depends on the message size.…”

Section: Motivationmentioning

confidence: 99%

Towards Lower Bounds on Embedding Distortion in Information Hiding

Kim

Duric

Richards

2006

Digital Watermarking

View full text Add to dashboard Cite

Abstract. We propose two efficient information hiding algorithms in the least significant bits of JPEG coefficients of images. Our algorithms embed information by modifying JPEG coefficients in such a way that the introduced distortion is minimized. We derive the expected value of the additional error due to distortion as a function of the message length and the probability distribution of the JPEG quantization errors of cover images. We have implemented our methods in Java and performed the extensive experiments with them. The experiments have shown that our theoretical predictions agree closely with the actual introduced distortions.

show abstract

“…For example, in order to calculate f Tp of the parameter s6, the bit sequence S 6 N is constructed using Equation (4), where N = 152790×8, 152790 is the least number of the frames the tested speech sample is divided into, which is the threshold value determined by observing a number of experiments, and 8 is the number of bits in the parameter s6 that can be used to embed messages. Hence L = 8 and N/L = 152790 are obtained; M = N/L represents the number of the tested frames.…”

Section: Poker Test For Speech Parametersmentioning

confidence: 99%

“…There has been quite some effort to study the steganalysis of digital images, and recent publications are [2][3] [4][5] [6]. In contrast to image steganography and steganalysis ， audio steganography and steganalysis are largely unexplored.…”

Section: Introductionmentioning

confidence: 99%