Robust template matching for affine resistant image watermarks

By S. Pereira, Thierry Pun. IEEE Transactions on Image Processing, 2000

621 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/4210f36543a826e6dd77f621aa4fa31bb2977d12

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Graphics and Computer-Aided Design

1. Ben Mildenhall
2. Justin Johnson
3. Matthew Tancik
4. Heung-Kyu Lee
5. Jiren Zhu
6. Hyung-Myong Kim
7. Russell Kaplan
8. Ren Ng

In Computer Vision and Pattern Recognition

1. Andreas Geiger
2. Luc Van Gool
3. Justin Johnson
4. Li Fei-Fei
5. Chen Change Loy
6. Ming-Hsuan Yang
7. Andrea Vedaldi
8. Trevor Darrell

Citations in the corpus

• HiDDeN: Hiding Data With Deep Networks (2018, methodology): “Other methods combine frequency-domain encoding with log-polar mapping [16] or template matching [14] to achieve robustness against spatial domain transformations.”
• Invariant image watermark using Zernike moments (2003, result): “Since the recognition of the need for watermarking schemes resilient to geometric distortion, many research results were reported [4]–[6], [8].”
• Data-Hiding Codes (2005, background): “• Pilot sequences [62], [96]–[102] for estimating desynchronization parameters.”
• StegaStamp: Invisible Hyperlinks in Physical Photographs (2019, background): “Later research demonstrated the significant robustness benefits of encoding the watermark in the log-polar frequency domain [27, 33, 35, 53].”