Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification
Abstract
:1. Introduction
- They can undergo several tampering attempts such as interception of communication, data corruption, forgery, and cloning.
- Systems as simple as serial numbers or bar codes are quite simple to forge or reproduce.
- Small components produced in large quantities, such as screws, would be made more expensive by the insertion, in each of them, of an RFID or NFC tag or other identification object. For even smaller components, it may be impossible to incorporate any type of identifier.
- The combination of label materials with those of the component could lead to obstacles at recycling.
- allows for the reduction in noise and non-relevant data at an early stage of the process;
- tests and compares a set of hashing methods and image-processing techniques for the treatment of cork stopper images;
- proposes the optimized design for the cork stopper authentication process.
2. Background of Concepts
2.1. Bicubic Interpolation on Rescaling
2.2. Gaussian Filter
2.3. Cropping
2.4. Contrast Stretching
2.5. Discrete Cosine Transform (DCT)
2.6. Local Linear Embedding (LLE)
2.7. Discrete Wavelet Transform (DWT)
2.8. Difference Hash (DH)
2.9. Average Hash (AH)
2.10. Radon Transform (RT)
2.11. Hough Transform (HT)
2.12. Hamming Distance
3. State of the Art
4. Methodology
5. Results and Discussion
- S1: Hash codes from all images in the database
- S2: Hash codes from images successfully rotated by RT (0° or 180°)
- S3: Hash codes from images successfully rotated by RT to the correct side (0°)
5.1. Testing Methods without Pre-Processing
General Results and Comparison for the Different Methods
5.2. Rotation Correction
5.2.1. Rotation Correction Using Hough Transform Operation
5.2.2. Rotation Correction Using Radon Transform
5.3. Contrast Stretching and Hash sizes
5.4. Time Performance
- Step 1: Image acquisition
- Step 2: Pre-processing (Gauss filter, contrast stretching, cubic interpolation)
- Step 3: Radon transform and angle calculation
- Step 4: Image-to-hash conversion
- Step 5: Hamming distance and final correspondence
6. Conclusions and Future Work
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Appendix A
Appendix B. Tables of Accuracy Results
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 6.8% | 6.5% | 1.3% | 5.0% | 0.8% |
Top 5 | 12.3% | 10.8% | 3.3% | 14.8% | 4.3% | |
Top 10 | 16.0% | 12.3% | 5.0% | 21.0% | 9.3% | |
S2 | Top 1 | 8.5% | 7.1% | 1.2% | 6.2% | 1.2% |
Top 5 | 13.8% | 11.5% | 4.1% | 17.4% | 5.9% | |
Top 10 | 20.3% | 14.7% | 5.0% | 23.5% | 10.0% | |
S3 | Top 1 | 11.8% | 7.8% | 2.9% | 3.9% | 5.9% |
Top 5 | 23.5% | 13.7% | 7.8% | 19.6% | 18.6% | |
Top 10 | 30.4% | 20.6% | 12.7% | 25.5% | 31.4% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 3.8% | 1.5% | 0.0% | 4.8% | 3.8% |
Top 5 | 7.3% | 4.0% | 2.5% | 11.3% | 9.0% | |
Top 10 | 11.0% | 5.5% | 4.3% | 17.0% | 10.8% | |
S2 | Top 1 | 4.1% | 1.5% | 0.0% | 5.0% | 4.1% |
Top 5 | 8.5% | 3.8% | 2.9% | 10.9% | 8.8% | |
Top 10 | 12.6% | 7.4% | 5.0% | 15.3% | 11.5% | |
S3 | Top 1 | 5.9% | 2.9% | 2.9% | 12.7% | 11.8% |
Top 5 | 22.5% | 13.7% | 9.8% | 24.5% | 22.5% | |
Top 10 | 35.3% | 22.5% | 20.6% | 37.3% | 35.3% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 5.3% | 21.8% | 9.0% | 31.8% | 9.3% |
Top 5 | 13.3% | 30.0% | 14.8% | 43.3% | 15.0% | |
Top 10 | 19.0% | 33.5% | 19.0% | 49.3% | 19.8% | |
S2 | Top 1 | 7.9% | 25.3% | 10.9% | 37.6% | 10.6% |
Top 5 | 17.4% | 35.0% | 18.2% | 48.5% | 18.8% | |
Top 10 | 25.0% | 38.8% | 21.8% | 55.0% | 24.7% | |
S3 | Top 1 | 15.7% | 35.3% | 17.6% | 47.1% | 19.6% |
Top 5 | 40.2% | 45.1% | 33.3% | 59.8% | 33.3% | |
Top 10 | 50.0% | 51.0% | 42.2% | 62.7% | 47.1% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 15.8% | 45.3% | 12.8% | 62.8% | 15.3% |
Top 5 | 30.3% | 54.8% | 16.8% | 74.3% | 25.5% | |
Top 10 | 37.8% | 61.8% | 20.3% | 77.3% | 31.5% | |
S2 | Top 1 | 20.6% | 53.8% | 15.6% | 72.4% | 17.9% |
Top 5 | 37.9% | 66.2% | 20.9% | 84.7% | 30.9% | |
Top 10 | 48.2% | 72.9% | 24.7% | 87.9% | 37.1% | |
S3 | Top 1 | 33.3% | 72.5% | 18.6% | 88.2% | 32.4% |
Top 5 | 52.9% | 85.3% | 29.4% | 93.1% | 53.9% | |
Top 10 | 67.6% | 87.3% | 39.2% | 96.1% | 61.8% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 3.3% | 16.5% | 1.3% | 9.3% | 15.5% |
Top 5 | 11.5% | 28.8% | 3.5% | 21.5% | 27.5% | |
Top 10 | 17.8% | 37.5% | 6.3% | 29.0% | 37.3% | |
S2 | Top 1 | 3.8% | 20.0% | 1.8% | 10.9% | 17.9% |
Top 5 | 14.1% | 35.0% | 4.7% | 25.3% | 32.1% | |
Top 10 | 20.3% | 45.0% | 7.9% | 34.4% | 43.5% | |
S3 | Top 1 | 7.8% | 33.3% | 2.9% | 18.6% | 20.6% |
Top 5 | 17.6% | 48.0% | 10.8% | 40.2% | 42.2% | |
Top 10 | 35.3% | 65.7% | 13.7% | 52.0% | 52.9% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 2.8% | 15.0% | 1.0% | 9.5% | 9.0% |
Top 5 | 7.5% | 28.8% | 4.0% | 22.0% | 25.3% | |
Top 10 | 12.3% | 36.8% | 7.5% | 27.3% | 33.8% | |
S2 | Top 1 | 4.1% | 19.1% | 1.8% | 9.1% | 10.3% |
Top 5 | 10.0% | 33.5% | 5.3% | 24.7% | 30.3% | |
Top 10 | 14.4% | 39.4% | 10.6% | 29.1% | 39.7% | |
S3 | Top 1 | 7.8% | 36.3% | 4.9% | 24.5% | 23.5% |
Top 5 | 27.5% | 58.8% | 13.7% | 45.1% | 48.0% | |
Top 10 | 32.4% | 73.5% | 24.5% | 66.7% | 58.8% |
DCT+LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 48.5% | 78.8% | 17.8% | 73.5% | 65.0% |
Top 5 | 67.0% | 84.0% | 28.5% | 81.5% | 77.0% | |
Top 10 | 73.0% | 84.8% | 34.5% | 84.3% | 80.8% | |
S2 | Top 1 | 57.6% | 90.3% | 12.4% | 84.1% | 75.0% |
Top 5 | 76.2% | 95.3% | 21.5% | 92.6% | 88.2% | |
Top 10 | 82.4% | 96.5% | 27.1% | 94.7% | 91.8% | |
S3 | Top 1 | 70.6% | 92.2% | 25.5% | 90.2% | 78.4% |
Top 5 | 87.3% | 95.1% | 45.1% | 93.1% | 90.2% | |
Top 10 | 91.2% | 96.1% | 57.8% | 98.0% | 95.1% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 45.0% | 78.8% | 22.5% | 76.5% | 68.3% |
Top 5 | 64.3% | 84.0% | 32.5% | 86.5% | 79.3% | |
Top 10 | 72.5% | 85.0% | 37.3% | 86.8% | 82.3% | |
S2 | Top 1 | 52.1% | 88.8% | 16.8% | 87.1% | 77.6% |
Top 5 | 73.5% | 93.5% | 22.4% | 96.2% | 88.5% | |
Top 10 | 82.9% | 95.0% | 27.9% | 96.5% | 92.4% | |
S3 | Top 1 | 63.7% | 96.1% | 34.3% | 93.1% | 84.3% |
Top 5 | 86.3% | 98.0% | 50.0% | 96.1% | 94.1% | |
Top 10 | 94.1% | 98.0% | 60.8% | 96.1% | 96.1% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 0.5% | 85.3% | 51.8% | 85.0% | 82.0% |
Top 5 | 0.5% | 85.8% | 63.8% | 86.3% | 85.0% | |
Top 10 | 7.0% | 86.5% | 69.8% | 87.0% | 85.3% | |
S2 | Top 1 | 0.6% | 97.1% | 59.4% | 96.5% | 93.2% |
Top 5 | 0.6% | 97.4% | 73.8% | 98.2% | 96.8% | |
Top 10 | 8.2% | 97.9% | 80.0% | 98.5% | 97.1% | |
S3 | Top 1 | 5.4% | 93.1% | 65.7% | 98.0% | 90.2% |
Top 5 | 14.3% | 96.1% | 82.4% | 98.0% | 95.1% | |
Top 10 | 21.4% | 97.1% | 86.3% | 98.0% | 96.1% |
DCT + LLE | pHash | aHash | dHash | wHash | ||
---|---|---|---|---|---|---|
S1 | Top 1 | 0.3% | 83.8% | 51.3% | 82.8% | 82.3% |
Top 5 | 0.5% | 85.5% | 64.5% | 84.0% | 86.5% | |
Top 10 | 0.5% | 86.3% | 70.0% | 85.5% | 87.5% | |
S2 | Top 1 | 0.3% | 93.5% | 57.4% | 92.1% | 92.4% |
Top 5 | 0.6% | 94.7% | 73.8% | 93.5% | 96.5% | |
Top 10 | 0.9% | 95.3% | 80.9% | 94.7% | 97.1% | |
S3 | Top 1 | 0.0% | 96.1% | 76.5% | 94.1% | 91.2% |
Top 5 | 0.0% | 96.1% | 84.3% | 96.1% | 96.1% | |
Top 10 | 0.0% | 96.1% | 88.2% | 98.0% | 96.1% |
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Section 2 | Parameter Designation | Value |
---|---|---|
Section 2.1 | Image size | 512 |
Section 2.2 | Lowpass filtering window size | 3 |
Section 2.2 | Standard deviation | 8 |
Section 2.3 | Rad. Minimum limit value | 230 |
Section 2.3 | Rad. Maximum limit value | 260 |
Section 2.3 | Center min. Limit value | 230 |
Section 2.3 | Center max. Limit value | 280 |
Section 2.4 | Clip value | 0.03 |
Section 2.5 | B (N = 64-bit hash code) | 64 |
Section 2.5 | B (N = 256-bit hash code) | 32 |
Section 2.5 | B (N = 1024-bit hash code) | 16 |
Section 2.6 | K (N = 64-bit hash code) | 15 |
Section 2.6 | Dmax (N = 64-bit hash code) | 40 |
Section 2.6 | K (N = 256-bit hash code) | 42 |
Section 2.6 | Dmax (N = 256-bit hash code) | 42 |
Section 2.6 | K (N = 1024-bit hash code) | 400 |
Section 2.6 | Dmax (N = 1024-bit hash code) | 450 |
Step 1 | Step 2 | Step 3 | Step 5 | ||||
---|---|---|---|---|---|---|---|
μ (s) | σ (s) | μ (s) | σ (s) | μ (s) | σ (s) | μ (s) | σ (s) |
0.0243 | 0.0131 | 0.8178 | 0.0765 | 7.8382 | 0.4197 | 0.0035 | 0.0065 |
64-bit | 256-bit | 1024-bit | ||||
---|---|---|---|---|---|---|
μ (s) | σ (s) | μ (s) | σ (s) | μ (s) | σ (s) | |
pHash | 0.0079 | 0.0250 | 0.0076 | 0.0192 | 0.0113 | 0.0252 |
aHash | 0.0071 | 0.0377 | 0.0113 | 0.0732 | 0.0086 | 0.0156 |
wHash | 0.0774 | 0.0235 | 0.1010 | 0.0420 | 0.1330 | 0.6917 |
dHash | 0.0041 | 0.0057 | 0.0050 | 0.0108 | 0.0065 | 0.0055 |
DCT + LLE | 17.3090 | 1.9727 | 19.4306 | 0.5628 | 274.3112 | 83.3862 |
Method | Literature [44], CBIR (ms) | Current Work, Hashing (ms) | Ratio |
---|---|---|---|
Average of Comparing Time | 9.6 | 0.241 | 39.83 |
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Fitas, R.; Rocha, B.; Costa, V.; Sousa, A. Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification. J. Imaging 2021, 7, 48. https://doi.org/10.3390/jimaging7030048
Fitas R, Rocha B, Costa V, Sousa A. Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification. Journal of Imaging. 2021; 7(3):48. https://doi.org/10.3390/jimaging7030048
Chicago/Turabian StyleFitas, Ricardo, Bernardo Rocha, Valter Costa, and Armando Sousa. 2021. "Design and Comparison of Image Hashing Methods: A Case Study on Cork Stopper Unique Identification" Journal of Imaging 7, no. 3: 48. https://doi.org/10.3390/jimaging7030048