Detecting LSB Steganography in Color and Gray-Scale Images
IEEE MultiMedia
Radiometric CCD camera calibration and noise estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Attacks on Steganographic Systems
IH '99 Proceedings of the Third International Workshop on Information Hiding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Local Approximation Techniques in Signal and Image Processing (SPIE Press Monograph Vol. PM157)
Local Approximation Techniques in Signal and Image Processing (SPIE Press Monograph Vol. PM157)
Fast communication: Steganography and error-correcting codes
Signal Processing
Locating steganographic payload via ws residuals
Proceedings of the 10th ACM workshop on Multimedia and security
Quantization Noise: Roundoff Error in Digital Computation, Signal Processing, Control, and Communications
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Digital Watermarking and Steganography
Digital Watermarking and Steganography
Generalised category attack: improving histogram-based attack on JPEG LSB embedding
IH'07 Proceedings of the 9th international conference on Information hiding
Classification of steganalysis techniques: A study
Digital Signal Processing
Advanced Statistical Steganalysis
Advanced Statistical Steganalysis
"Break our steganographic system": the ins and outs of organizing BOSS
IH'11 Proceedings of the 13th international conference on Information hiding
Statistical decision methods in hidden information detection
IH'11 Proceedings of the 13th international conference on Information hiding
A cover image model for reliable steganalysis
IH'11 Proceedings of the 13th international conference on Information hiding
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Towards multi-class blind steganalyzer for JPEG images
IWDW'05 Proceedings of the 4th international conference on Digital Watermarking
An improved sample pairs method for detection of LSB embedding
IH'04 Proceedings of the 6th 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
Detection of LSB steganography via sample pair analysis
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Detection of hiding in the least significant bit
IEEE Transactions on Signal Processing - Part II
A Neyman-Pearson approach to statistical learning
IEEE Transactions on Information Theory
Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images
IEEE Transactions on Image Processing
Low Bit-Rate Image Coding Using Adaptive Geometric Piecewise Polynomial Approximation
IEEE Transactions on Image Processing
Practical Poissonian-Gaussian Noise Modeling and Fitting for Single-Image Raw-Data
IEEE Transactions on Image Processing
-Optimal Non-Bayesian Anomaly Detection for Parametric Tomography
IEEE Transactions on Image Processing
Adaptive Steganalysis of Least Significant Bit Replacement in Grayscale Natural Images
IEEE Transactions on Signal Processing
Steganalysis of LSB replacement using parity-aware features
IH'12 Proceedings of the 14th international conference on Information Hiding
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This paper studies the detection of Least Significant Bits (LSB) steganography in digital media by using hypothesis testing theory. The main goal is threefold: first, it is aimed to design a test whose statistical properties are known, this especially allows the guaranteeing of a false alarm probability. Second, the quantization of samples is studied throughout this paper. Lastly, the use of a linear parametric model of samples is used to estimate unknown parameters and design a test which can be used when no information on cover medium is available. To this end, the steganalysis problem is cast within the framework of hypothesis testing theory and digital media are considered as quantized signals. In a theoretical context where media parameters are assumed to be known, the Likelihood Ratio Test (LRT) is presented. Its statistical performances are analytically established; this highlights the impact of quantization on the most powerful steganalyzer. In a practical situation, when image parameters are unknown, a Generalized LRT (GLRT) is proposed based on a local linear parametric model of samples. The use of such model allows us to establish GLRT statistical properties in order to guarantee a prescribed false-alarm probability. Focusing on digital images, it is shown that the well-known WS (Weighted-Stego) is close to the proposed GLRT using a specific model of cover image. Finally, numerical results on natural images show the relevance of theoretical findings.