| Title | A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Qin, Xinghong, Li, Bin, Huang, Jiwu |
| Conference Name | ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
| Date Published | may |
| Keywords | adaptive filtering, approximated Fisher Information, Computational modeling, content-adaptive image steganographic schemes, feature extraction, gaussian distribution, high-pass filtering, high-pass filters, Image coding, image filtering, image residuals modeling, information processing, Media, Metrics, multivariate Gaussian model, Payloads, pubcrawl, quantized multivariate Gaussian distribution, Resiliency, Scalability, security, spatial images, spatial steganographic scheme, steganalysis, steganographic methods, steganography |
| Abstract | Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method. |
| DOI | 10.1109/ICASSP.2019.8682688 |
| Citation Key | qin_new_2019 |
A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model