Minimizing the embedding impact in steganography
MM&Sec '06 Proceedings of the 8th workshop on Multimedia and security
Least significant bit steganography detection with machine learning techniques
Proceedings of the 2007 international workshop on Domain driven data mining
Statistically undetectable jpeg steganography: dead ends challenges, and opportunities
Proceedings of the 9th workshop on Multimedia & security
Towards digital video steganalysis using asymptotic memoryless detection
Proceedings of the 9th workshop on Multimedia & security
A Data Mapping Method for Steganography and Its Application to Images
Information Hiding
Benchmarking for Steganography
Information Hiding
Fusion Based Blind Image Steganalysis by Boosting Feature Selection
IWDW '07 Proceedings of the 6th International Workshop on Digital Watermarking
Detection of seam carving and localization of seam insertions in digital images
Proceedings of the 11th ACM workshop on Multimedia and security
YASS: yet another steganographic scheme that resists blind steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
A fusion of maximum likelihood and structural steganalysis
IH'07 Proceedings of the 9th international conference on Information hiding
A review of the audio and video steganalysis algorithms
Proceedings of the 48th Annual Southeast Regional Conference
Steganalysis using partially ordered Markov models
IH'10 Proceedings of the 12th international conference on Information hiding
KL-sense secure image steganography
International Journal of Security and Networks
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The difficult task of steganalysis, or the detection of the presence of hidden data, can be greatly aided by exploiting the correlations inherent in typical host or cover signals. In particular, several effective image steganalysis techniques are based on the strong interpixel dependencies exhibited by natural images. Thus, existing theoretical benchmarks based on independent and identically distributed (i.i.d.) models for the cover data underestimate attainable steganalysis performance and, hence, overestimate the security of the steganography technique used for hiding the data. In this paper, we investigate detection-theoretic performance benchmarks for steganalysis when the cover data are modeled as a Markov chain. The main application explored here is steganalysis of data hidden in images. While the Markov chain model does not completely capture the spatial dependencies, it provides an analytically tractable framework whose predictions are consistent with the performance of practical steganalysis algorithms that account for spatial dependencies. Numerical results are provided for image steganalysis of spread-spectrum and perturbed quantization data hiding.