Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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)
Digital Watermarking and Steganography
Digital Watermarking and Steganography
The 'Dresden Image Database' for benchmarking digital image forensics
Proceedings of the 2010 ACM Symposium on Applied Computing
Edge adaptive image steganography based on LSB matching revisited
IEEE Transactions on Information Forensics and Security
Steganalysis by subtractive pixel adjacency matrix
IEEE Transactions on Information Forensics and Security
Advanced Statistical Steganalysis
Advanced Statistical Steganalysis
Using high-dimensional image models to perform highly undetectable steganography
IH'10 Proceedings of the 12th international conference on Information hiding
"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
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
Detection of hiding in the least significant bit
IEEE Transactions on Signal Processing - Part II
Steganalysis for Markov cover data with applications to images
IEEE Transactions on Information Forensics and Security
Adaptive Data Hiding in Edge Areas of Images With Spatial LSB Domain Systems
IEEE Transactions on Information Forensics and Security
Singularity detection and processing with wavelets
IEEE Transactions on Information Theory - Part 2
Performance Measures for Neyman–Pearson Classification
IEEE Transactions on Information Theory
Rate-distortion optimized tree-structured compression algorithms for piecewise polynomial images
IEEE Transactions on Image Processing
Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries
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
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 proposes a novel methodology to detect data hidden in the least significant bits of a natural image. The goal is twofold: first, the methodology aims at proposing a test specifically designed for natural images, to this end an original model of images is presented, and, second, the statistical properties of the designed test, probability of false alarm and power function, should be predictable. The problem of hidden data detection is set in the framework of hypothesis testing theory. When inspected image parameters are known, the Likelihood Ratio Test (LRT) is designed and its statistical performance is analytically established. In practice, unknown image parameters have to be estimated. The proposed model of natural images is used to estimate unknown parameters accurately and to design a Generalized Likelihood Ratio Test (GLRT). Finally, the statistical properties of the proposed GLRT are analytically established which permits us, first, to guarantee a prescribed false-alarm probability and, second, to show that the GLRT is almost as powerful as the optimal LRT. Numerical results on natural image databases and comparison with prior art steganalyzers show the relevance of theoretical findings.