Visible to the public Quantized Gaussian Embedding Steganography

TitleQuantized Gaussian Embedding Steganography
Publication TypeConference Paper
Year of Publication2019
AuthorsSharifzadeh, Mehdi, Aloraini, Mohammed, Schonfeld, Dan
Conference NameICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN Number978-1-4799-8131-1
Keywordscomposability, cost-based steganography, detection error, Detectors, distortion, gaussian distribution, Gaussian embedding, Gaussian embedding method, Gaussian processes, generalized adopted statistical model, hypothesis testing, image processing, Image Steganography, Mathematical model, Metrics, multivariate Gaussian random variables, optimal detector, Payloads, privacy, pubcrawl, quantized Gaussian embedding steganography, Random variables, statistical analysis, statistical framework, steganography, steganography detection, stego messages, Testing, universal embedding scheme, variance estimators

In this paper, we develop a statistical framework for image steganography in which the cover and stego messages are modeled as multivariate Gaussian random variables. By minimizing the detection error of an optimal detector within the generalized adopted statistical model, we propose a novel Gaussian embedding method. Furthermore, we extend the formulation to cost-based steganography, resulting in a universal embedding scheme that works with embedding costs as well as variance estimators. Experimental results show that the proposed approach avoids embedding in smooth regions and significantly improves the security of the state-of-the-art methods, such as HILL, MiPOD, and S-UNIWARD.

Citation Keysharifzadeh_quantized_2019