Name
Abstract | ||
|---|---|---|
Image watermarking is usually decomposed into three steps: i) some features are extracted from an image, ii) they are modified to embed the watermark, iii) and they are projected back into the image space while avoiding the creation of visual artefacts. The feature extraction is usually based on a classical image representation given by the Discrete Wavelet Transform or the Discrete Cosine Transform for instance. These transformations need a very accurate synchronisation and usually rely on various registration mechanisms for that purpose. This paper investigates a new family of transformation based on Deep Learning networks. Motivations come from the Computer Vision literature which has demonstrated the robustness of these features against light geometric distortions. Also, adversarial sample literature provides means to implement the inverse transform needed in the third step. This paper shows that this approach is feasible as it yields a good quality of the watermarked images and an intrinsic robustness. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/WIFS.2018.8630768 | 2018 IEEE International Workshop on Information Forensics and Security (WIFS) |
Keywords | Field | DocType |
watermarked images,Computer Vision literature,Deep Learning networks,registration mechanisms,transformations,Discrete Cosine Transform,Discrete Wavelet Transform,classical image representation,feature extraction,visual artefacts,image space,blind image watermarking,Deep neural networks | Computer vision,Synchronization,Digital watermarking,Computer science,Discrete cosine transform,Feature extraction,Robustness (computer science),Watermark,Discrete wavelet transform,Artificial intelligence,Deep learning | Conference |
ISSN | ISBN | Citations |
2157-4766 | 978-1-5386-6537-4 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vedran Vukotic | 1 | 29 | 4.59 |
| Vivien Chappelier | 2 | 0 | 0.34 |
| Teddy Furon | 3 | 660 | 55.04 |