Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT
This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6554 |
| _version_ | 1797515733478932480 |
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| author | Li Li Rui Bai Shanqing Zhang Chin-Chen Chang Mengtao Shi |
| author_facet | Li Li Rui Bai Shanqing Zhang Chin-Chen Chang Mengtao Shi |
| author_sort | Li Li |
| collection | DOAJ |
| description | This paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications. |
| first_indexed | 2024-03-10T06:51:28Z |
| format | Article |
| id | doaj.art-bca7dbd409bf427db0ceabc7a01505c3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:28Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bca7dbd409bf427db0ceabc7a01505c32023-11-22T16:47:48ZengMDPI AGSensors1424-82202021-09-012119655410.3390/s21196554Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QTLi Li0Rui Bai1Shanqing Zhang2Chin-Chen Chang3Mengtao Shi4School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaDepartment of Information Engineering and Computer Science, Feng Chia University, Taichung 40724, TaiwanSchool of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, ChinaThis paper proposes a screen-shooting resilient watermarking scheme via learned invariant keypoints and QT; that is, if the watermarked image is displayed on the screen and captured by a camera, the watermark can be still extracted from the photo. A screen-shooting resilient watermarking algorithm should meet the following two basic requirements: robust keypoints and a robust watermark algorithm. In our case, we embedded watermarks by combining the feature region filtering model to SuperPoint (FRFS) neural networks, quaternion discrete Fourier transform (QDFT), and tensor decomposition (TD). First we applied FRFS to locate the embedding feature regions which are decided by the keypoints that survive screen-shooting. Second, we structured watermark embedding regions centered at keypoints. Third, the watermarks were embedded by the QDFT and TD (QT) algorithm, which is robust for capturing process attacks. In a partial shooting scenario, the watermark is repeatedly embedded into different regions in an image to enhance robustness. Finally, we extracted the watermarks from at least one region at the extraction stage. The experimental results showed that the proposed scheme is very robust for camera shooting (including partial shooting) different shooting scenarios, and special attacks. Moreover, the efficient mechanism of screen-shooting resilient watermarking could have propietary protection and leak tracing applications.https://www.mdpi.com/1424-8220/21/19/6554screen-shootingFRFSQTrobustnesspartial shooting |
| spellingShingle | Li Li Rui Bai Shanqing Zhang Chin-Chen Chang Mengtao Shi Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT Sensors screen-shooting FRFS QT robustness partial shooting |
| title | Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT |
| title_full | Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT |
| title_fullStr | Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT |
| title_full_unstemmed | Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT |
| title_short | Screen-Shooting Resilient Watermarking Scheme via Learned Invariant Keypoints and QT |
| title_sort | screen shooting resilient watermarking scheme via learned invariant keypoints and qt |
| topic | screen-shooting FRFS QT robustness partial shooting |
| url | https://www.mdpi.com/1424-8220/21/19/6554 |
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