Heteroscedastic watermark detector in the contourlet domain
A new contourlet domain image watermark detector is proposed in the present study. As the performance of the detector completely depends on the accuracy of the statistical model, the contourlet coefficients and statistical properties are studied first. The heavy‐tailed distribution and heteroscedast...
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-04-01
|
| Series: | IET Computer Vision |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-cvi.2018.5254 |
| _version_ | 1797684181500690432 |
|---|---|
| author | Maryam Amirmazlaghani |
| author_facet | Maryam Amirmazlaghani |
| author_sort | Maryam Amirmazlaghani |
| collection | DOAJ |
| description | A new contourlet domain image watermark detector is proposed in the present study. As the performance of the detector completely depends on the accuracy of the statistical model, the contourlet coefficients and statistical properties are studied first. The heavy‐tailed distribution and heteroscedasticity of these coefficients are demonstrated in this study. These characteristics cannot be captured simultaneously by the models, which are proposed previously. A two‐dimensional generalised autoregressive conditional heteroscedasticity (2D GARCH) model is suggested to overcome this problem. Dependencies of the contourlet coefficients can be explained by the efficient structure provided by this model. A 2D GARCH model‐based contourlet domain watermark detector is designed and its performance analysed by computing the receiver operating characteristics. The high accuracy of the proposed detector, its robustness under several types of attacks, and its outperformance compared to alternative watermarking methods are verified by the obtained experimental results. |
| first_indexed | 2024-03-12T00:25:51Z |
| format | Article |
| id | doaj.art-1214db2ed9744499922e498b31c31a19 |
| institution | Directory Open Access Journal |
| issn | 1751-9632 1751-9640 |
| language | English |
| last_indexed | 2024-03-12T00:25:51Z |
| publishDate | 2019-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Computer Vision |
| spelling | doaj.art-1214db2ed9744499922e498b31c31a192023-09-15T10:35:47ZengWileyIET Computer Vision1751-96321751-96402019-04-0113324926010.1049/iet-cvi.2018.5254Heteroscedastic watermark detector in the contourlet domainMaryam Amirmazlaghani0Department of Computer Engineering and Information TechnologyAmirkabir University of TechnologyTehranIranA new contourlet domain image watermark detector is proposed in the present study. As the performance of the detector completely depends on the accuracy of the statistical model, the contourlet coefficients and statistical properties are studied first. The heavy‐tailed distribution and heteroscedasticity of these coefficients are demonstrated in this study. These characteristics cannot be captured simultaneously by the models, which are proposed previously. A two‐dimensional generalised autoregressive conditional heteroscedasticity (2D GARCH) model is suggested to overcome this problem. Dependencies of the contourlet coefficients can be explained by the efficient structure provided by this model. A 2D GARCH model‐based contourlet domain watermark detector is designed and its performance analysed by computing the receiver operating characteristics. The high accuracy of the proposed detector, its robustness under several types of attacks, and its outperformance compared to alternative watermarking methods are verified by the obtained experimental results.https://doi.org/10.1049/iet-cvi.2018.5254two-dimensional generalised autoregressive conditional heteroscedasticity modelheavy-tailed distributionstatistical propertiescontourlet coefficientsstatistical modelcontourlet domain image watermark detector |
| spellingShingle | Maryam Amirmazlaghani Heteroscedastic watermark detector in the contourlet domain IET Computer Vision two-dimensional generalised autoregressive conditional heteroscedasticity model heavy-tailed distribution statistical properties contourlet coefficients statistical model contourlet domain image watermark detector |
| title | Heteroscedastic watermark detector in the contourlet domain |
| title_full | Heteroscedastic watermark detector in the contourlet domain |
| title_fullStr | Heteroscedastic watermark detector in the contourlet domain |
| title_full_unstemmed | Heteroscedastic watermark detector in the contourlet domain |
| title_short | Heteroscedastic watermark detector in the contourlet domain |
| title_sort | heteroscedastic watermark detector in the contourlet domain |
| topic | two-dimensional generalised autoregressive conditional heteroscedasticity model heavy-tailed distribution statistical properties contourlet coefficients statistical model contourlet domain image watermark detector |
| url | https://doi.org/10.1049/iet-cvi.2018.5254 |
| work_keys_str_mv | AT maryamamirmazlaghani heteroscedasticwatermarkdetectorinthecontourletdomain |