An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data
Abstract
:1. Introduction
2. The Multiple Watermarking Algorithm
2.1. Watermark Generation
2.2. Watermark Embedding
2.3. Watermark Extraction and Detection
2.3.1. Watermark Extraction
2.3.2. Watermark Detection
2.3.3. Applicability Analysis
- (1)
- Randomly divide the vertices in the cover data into two non-repetitive sets.
- (2)
- Generate two watermarks, watermark 1 and watermark 2, and then embed the two watermarks in the two sets.
- (3)
- Extract the watermark, according to Equation (3); the corresponding detection is done by using watermark 1 and watermark 2. The correlation detection coefficients are cor3 and cor4.
- (4)
- Normalize cor3 and cor4 according to Equation (19).
- (5)
- Generate two other watermarks, watermark 3 and watermark 4.
- (6)
- Watermark 3 and watermark 4 are used to carry out the corresponding detection with the extracted watermark according to Equation (3), to obtain the correlation detection coefficients cor1 and cor2.
- (7)
- Normalize cor1 and cor2 according to Equation (20).
- (8)
- Repeat steps (1) to (7) 1000 times and record the experimental results, cor1, cor2, cor3, and cor4.
3. Experimental Results and Discussion
3.1. Robustness Experiments
3.2. Discussion of Robustness against Crop** Attacks
3.3. Discussion of Detection Efficiency
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Attacks | Data Size | Detection Threshold | Watermarks | Detection Results |
---|---|---|---|---|
No attacks | 45,847 | 0.2040 | Watermark1 | 0.5156(√) |
Watermark2 | 0.5102(√) | |||
Watermark3 | −0.0156(×) | |||
Deleting vertex attacks | 32,093 | 0.2033 | Watermark1 | 0.5167(√) |
Watermark2 | 0.5049(√) | |||
Watermark3 | −0.0152(×) | |||
Deleting vertex attacks | 18,339 | 0.2037 | Watermark1 | 0.5091(√) |
Watermark2 | 0.5086(√) | |||
Watermark3 | −0.0121(×) | |||
Deleting vertex attacks | 4585 | 0.2129 | Watermark1 | 0.5258(√) |
Watermark2 | 0.5061(√) | |||
Watermark3 | −0.0133(×) | |||
Deleting vertex attacks | 917 | 0.2515 | Watermark1 | 0.5573(√) |
Watermark2 | 0.5005(√) | |||
Watermark3 | −0.0382(×) |
Attacks | Data Size | Detection Threshold | Watermarks | Detection Results |
---|---|---|---|---|
No attacks | 45,847 | 0.2040 | Watermark1 | 0.5156(√) |
Watermark2 | 0.5102(√) | |||
Watermark3 | −0.0156(×) | |||
Adding vertex attacks | 59,601 | 0.1572 | Watermark1 | 0.3962(√) |
Watermark2 | 0.3935(√) | |||
Watermark3 | −0.0131(×) | |||
Adding vertex attacks | 68,770 | 0.1366 | Watermark1 | 0.3446(√) |
Watermark2 | 0.3409(√) | |||
Watermark3 | −0.0099(×) | |||
Adding vertex attacks | 82,524 | 0.1145 | Watermark1 | 0.2898(√) |
Watermark2 | 0.2835(√) | |||
Watermark3 | −0.0058(×) | |||
Adding vertex attacks | 91,694 | 0.1023 | Watermark1 | 0.2550(√) |
Watermark2 | 0.2573(√) | |||
Watermark3 | −0.0058(×) |
Attacks | Data Size | Detection Threshold | Watermarks | Detection Results |
---|---|---|---|---|
No attacks | 45,847 | 0.2040 | Watermark1 | 0.5156(√) |
Watermark2 | 0.5102(√) | |||
Watermark3 | −0.0156(×) | |||
Compression attacks | 40,011 | 0.2045 | Watermark1 | 0.5176(√) |
Watermark2 | 0.5101(√) | |||
Watermark3 | −0.0139(×) | |||
Compression attacks | 36,073 | 0.2044 | Watermark1 | 0.5151(√) |
Watermark2 | 0.5117(√) | |||
Watermark3 | −0.0152(×) | |||
Compression attacks | 29,906 | 0.2048 | Watermark1 | 0.5134(√) |
Watermark2 | 0.5153(√) | |||
Watermark3 | −0.0135(×) | |||
Compression attacks | 21,425 | 0.2054 | Watermark1 | 0.5161√) |
Watermark2 | 0.5123(√) | |||
Watermark3 | −0.0196(×) | |||
Compression attacks | 12,063 | 0.2057 | Watermark1 | 0.5011(√) |
Watermark2 | 0.5210(√) | |||
Watermark3 | −0.0137(×) |
Attacks | Data Size | Detection Threshold | Watermarks | Detection Results |
---|---|---|---|---|
No attacks | 45,847 | 0.2040 | Watermark1 | 0.5156(√) |
Watermark2 | 0.5102(√) | |||
Watermark3 | −0.0156(×) | |||
Crop** attacks | 17,630 | 0.2039 | Watermark1 | 0.5114(√) |
Watermark2 | 0.5066(√) | |||
Watermark3 | −0.0187(×) | |||
Crop** attacks | 10,606 | 0.2053 | Watermark1 | 0.5019(√) |
Watermark2 | 0.5114(√) | |||
Watermark3 | −0.0239(×) | |||
Crop** attacks | 5191 | 0.2092 | Watermark1 | 0.4991(√) |
Watermark2 | 0.5122(√) | |||
Watermark3 | −0.0237(×) | |||
Crop** attacks | 1498 | 0.2187 | Watermark1 | 0.4793(√) |
Watermark2 | 0.4887(√) | |||
Watermark3 | 0.0561(×) | |||
Crop** attacks | 959 | 0.2452 | Watermark1 | 0.4953(√) |
Watermark2 | 0.5162(√) | |||
Watermark3 | −0.0636(×) |
Attacks | Data Size | Watermarks | Detection Results | ||
---|---|---|---|---|---|
Proposed Algorithm | Algorithm in Reference [27] | Algorithm in Reference [29] | |||
No attacks | 45,847 | Watermark1 | √ | √ | √ |
Watermark2 | √ | √ | √ | ||
Watermark3 | √ | √ | √ | ||
Watermark4 | √ | √ | √ | ||
Crop** attack (a) | 17,921 | Watermark1 | √ | √ | √ |
Watermark2 | √ | √ | √ | ||
Watermark3 | √ | √ | √ | ||
Watermark4 | √ | √ | √ | ||
Crop** attack (b) | 5320 | Watermark1 | √ | × | √ |
Watermark2 | √ | × | √ | ||
Watermark3 | √ | × | √ | ||
Watermark4 | √ | √ | √ | ||
Crop** attack (c) | 11,898 | Watermark1 | √ | × | √ |
Watermark2 | √ | × | √ | ||
Watermark3 | √ | √ | √ | ||
Watermark4 | √ | √ | √ | ||
Crop** attack (d) | 10,105 | Watermark1 | √ | × | √ |
Watermark2 | √ | √ | √ | ||
Watermark3 | √ | × | √ | ||
Watermark4 | √ | × | √ | ||
Crop** attack (e) | 12,646 | Watermark1 | √ | × | √ |
Watermark2 | √ | √ | √ | ||
Watermark3 | √ | √ | √ | ||
Watermark4 | √ | × | √ |
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Wang, Y.; Yang, C.; Zhu, C.; Ding, K. An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data. Information 2018, 9, 296. https://doi.org/10.3390/info9120296
Wang Y, Yang C, Zhu C, Ding K. An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data. Information. 2018; 9(12):296. https://doi.org/10.3390/info9120296
Chicago/Turabian StyleWang, Yingying, Chengsong Yang, Changqing Zhu, and Kaimeng Ding. 2018. "An Efficient Robust Multiple Watermarking Algorithm for Vector Geographic Data" Information 9, no. 12: 296. https://doi.org/10.3390/info9120296