Name
Abstract | ||
|---|---|---|
In this paper, an adaptive blind watermarking algorithm is proposed, which presents a new method of block classification based on the characteristic of visual masking of HVS (human visual system) model, including energy and variance. According to the new classification method, classify image blocks and select quantization pedometers adaptively to embed the watermark into DC component. At the same time, because of the shortage of watermark intensity only embedded into DC components, the algorithm presents that the same watermark is embedded into mid-frequency subbands by changing the order of these coefficients under invisibility. This is a blind watermarking algorithm, the watermark can be extracted without the original picture needed. Experimental results and attacks analysis show that the proposed algorithm is effective and robust to common image processing and some geometric attacks, such as JPEG lossy compression, scaling, additive noise, rotation, histogram equilibrium, contrast adjusting, brightness adjusting, filtering and so on. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IIH-MSP.2006.265120 | IIH-MSP |
Keywords | Field | DocType |
contrast adjusting,robust watermarking algorithm,dc component,watermark intensity,human visual system,attacks analysis show,block classification,blind watermarking algorithm,proposed algorithm,adaptive blind watermarking algorithm,common image processing,transform coding,watermarking,algorithm design and analysis,visual system,quantization,image processing,lossy compression,image analysis | Digital watermarking,Human visual system model,Computer science,Theoretical computer science,Watermark,Artificial intelligence,Computer vision,Algorithm design,Lossy compression,Algorithm,Transform coding,JPEG,Quantization (signal processing) | Conference |
ISBN | Citations | PageRank |
0-7695-2745-0 | 1 | 0.36 |
References | Authors | |
4 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaohua Qiao | 1 | 7 | 2.00 |
| Shuxun Wang | 2 | 31 | 8.61 |
| Quan Wen | 3 | 3 | 1.78 |
| Zhao Xu | 4 | 7 | 1.33 |