An improved sample pairs method for detection of LSB embedding

Authors:
Peizhong Lu;Xiangyang Luo;Qingyang Tang;Li Shen
Affiliations:
Department of Computer Science and Engineering, Fudan University, Shanghai, China;Institute of Information Engineering, Information and Engineering University, Zhengzhou, China;Department of Computer Science and Engineering, Fudan University, Shanghai, China;Institute of Information Engineering, Information and Engineering University, Zhengzhou, China
Venue:
IH'04 Proceedings of the 6th international conference on Information Hiding
Year:
2004

Citing 1
Cited 22

Detection of LSB steganography via sample pair analysis

IEEE Transactions on Signal Processing

LSB steganalysis based on high-order statistics

MM&Sec '05 Proceedings of the 7th workshop on Multimedia and security
The ultimate steganalysis benchmark?

Proceedings of the 9th workshop on Multimedia & security
A fast and effective method to detect multiple least significant bits steganography

Proceedings of the 2008 ACM symposium on Applied computing
Review: A review on blind detection for image steganography

Signal Processing
Locating steganographic payload via ws residuals

Proceedings of the 10th ACM workshop on Multimedia and security
Steganalytic Measures for the Steganography Using Pixel-Value Differencing and Modulus Function

PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Steganalysis of Multi Bit Plane Image Steganography

IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Digital Watermarking and Steganography

Digital Watermarking and Steganography
LTSB steganalysis based on quartic equation

Transactions on data hiding and multimedia security II
A fusion of maximum likelihood and structural steganalysis

IH'07 Proceedings of the 9th international conference on Information hiding
Generalised category attack: improving histogram-based attack on JPEG LSB embedding

IH'07 Proceedings of the 9th international conference on Information hiding
Quantitative steganalysis of LSB embedding in JPEG domain

Proceedings of the 12th ACM workshop on Multimedia and security
Quantitative structural steganalysis of Jsteg

IEEE Transactions on Information Forensics and Security
Assessment of steganalytic methods using multiple regression models

IH'05 Proceedings of the 7th international conference on Information Hiding
A general framework for structural steganalysis of LSB replacement

IH'05 Proceedings of the 7th international conference on Information Hiding
Parameter-estimation and algorithm-selection based United-Judgment for image steganalysis

Multimedia Tools and Applications
Equivalence analysis among DIH, SPA, and RS steganalysis methods

CMS'06 Proceedings of the 10th IFIP TC-6 TC-11 international conference on Communications and Multimedia Security
Multi bit plane image steganography

IWDW'06 Proceedings of the 5th international conference on Digital Watermarking
Steganalysis of LSB replacement using parity-aware features

IH'12 Proceedings of the 14th international conference on Information Hiding
Active steganalysis for interpolation-error based reversible data hiding

Pattern Recognition Letters
Hidden information detection using decision theory and quantized samples: Methodology, difficulties and results

Digital Signal Processing
A local adaptive model of natural images for almost optimal detection of hidden data

Signal Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a fast algorithm to detect the least significant bit (LSB) steganography. Our approach is inspired by the work of Dumitrescu et al.[1] who detected LSB steganography via sample pair analysis. Our new algorithm combines with the statistical measures developed in [1] and a new least square estimation. The motivation comes from the high accuracy and robustness of the least square method used in parameter estimation. Plentiful experimental results show that our novel method has much lower false alarm rate of 5% than that of 13.79% in [1]. Meanwhile, the estimating precision of our algorithm is about 9% higher than that of the algorithm [1] if the embedding ratio is less than 10%, and the speed of our algorithm is also about 15% faster than the algorithm [1]. Some theoretical derivations are also included.