A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform
Abstract
:1. Introduction
2. Related Work
2.1. Feature Matrix Based on SIFRT
2.2. Multi-Radius Local Binary Pattern Descriptors
2.3. Logistic Chaotic Encryption
3. Algorithm Process
3.1. Zero-Watermarking Generation Algorithm
Algorithm 1: Binary feature generation algorithm based on SIFRT |
- Select the original image requiring copyright protection. Utilize the SIFRT algorithm to statistically extract N stable keypoints from the carrier image under various attacks and calculate the descriptor arrays storing these keypoints. Reduce the dimensionality of their 128-dimensional SIFT descriptor vectors and remove redundant information to generate feature-rich vector matrices;
- Use the MrLBP algorithm to generate 36-dimensional MrLBP descriptor vectors and count the number of 0 s and 1 s in the vectors. Based on the number of 0 s and 1 s in the MrLBP descriptor vectors, determine the threshold size for the corresponding 92-dimensional keypoint feature vectors and binarize them to ensure roughly equal numbers of 0 s and 1 s in the concatenated 128-dimensional vectors. Combine and arrange the obtained keypoint feature vector matrix and local texture feature matrix to generate a binary feature matrix based on the SIFRT algorithm;
- Perform Arnold position scrambling and logistic chaos encryption on the watermarking information that is to be embedded. Conduct an XOR operation using the encrypted image and the final encrypted SIFRT binary feature matrix obtained in Algorithm 1 to obtain the zero-watermarking image. Apply for a timestamp from a reputable timestamp authority, bind the final zero-watermarking signal with this, and register the bound signal in the intellectual property right database (IPRD). The construction and registration process of the zero-watermarking signal is now complete.
3.2. Copyright Information Extraction Algorithm
- Preprocess the attacked copyright image that is to be verified and obtain the unencrypted SIFRT binary feature matrix using steps 1–2 in Section 3.1. Combine the encryption algorithm and key to perform chaotic encryption on the SIFRT binary feature matrix, facilitating subsequent XOR operations;
- Retrieve the corresponding zero-watermarking image information from a third-party intellectual property database. XOR this information with the SIFRT binary feature image encrypted with the cipher to generate the watermarking image that requires secondary decryption and a restoration of scrambling order;
- Decipher the watermarking image in ciphertext format obtained in the previous step to obtain an image containing copyright information symbols. Ensure that both subjective perception and objective indicators allow for the identification and differentiation of copyright information.
- In the case of multiple copyright disputes, apply for a timestamp from a timestamp authority to bind the date. This provides legal copyright protection.
4. Experimental Results and Analysis
4.1. Experimental Materials
4.2. Effectiveness and Security Test
4.3. Distinguishability Test
4.4. Distinguishability Test
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Median Filtering | Gaussian Noise | Salt and Pepper Noise | Partial Cropping | Clockwise Rotation 3° | Clockwise Rotation−3° | JPEG Compression | |
---|---|---|---|---|---|---|---|
Butterfly | 0.9714 | 0.9523 | 0.9797 | 0.9631 | 0.9795 | 0.9801 | 0.9944 |
Squirrel | 0.9868 | 0.9541 | 0.9812 | 0.9679 | 0.9857 | 0.9859 | 0.9997 |
Flower | 0.9957 | 0.9543 | 0.9837 | 0.9682 | 0.9832 | 0.9834 | 0.9998 |
Woman | 0.9943 | 0.9540 | 0.9807 | 0.9682 | 0.9854 | 0.9853 | 1 |
Attack Mode | Attack Intensity | Normalized Cross-Correlation | ||||||
---|---|---|---|---|---|---|---|---|
SIFRT | [14] | [15] | [16] | [17] | [18] | [19] | ||
Median filtering | 3 × 3 | 0.9957 | 0.9922 | 0.6939 | 0.9974 | 0.9954 | 0.9940 | 0.9872 |
Gaussian noise | 0.1 | 0.9543 | 0.9756 | 0.5961 | 0.9520 | 0.9374 | 0.9283 | 0.9455 |
Salt and pepper noise | 0.1 | 0.9837 | 0.9510 | 0.6191 | 0.9748 | 0.9687 | 0.9583 | 0.9637 |
Partial cropping | 1/16 | 0.9688 | 0.9360 | 0.7454 | 0.9721 | 0.9501 | 0.9792 | 0.8967 |
Clockwise rotation | 3° | 0.9832 | 0.8223 | 0.9387 | 0.9052 | 0.8691 | 0.9794 | 0.9437 |
Clockwise rotation | −3° | 0.9834 | 0.8209 | 0.7020 | 0.9126 | 0.8691 | 0.9786 | 0.9623 |
JPEG | 10 | 0.9164 | 0.9866 | 0.6403 | 0.9922 | 0.9749 | 0.9863 | 0.9872 |
JPEG | 50 | 0.9998 | 0.9952 | 0.7151 | 0.9957 | 0.9899 | 0.9976 | 0.9949 |
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Li, F.; Wang, Z.-X. A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform. Appl. Sci. 2024, 14, 4756. https://doi.org/10.3390/app14114756
Li F, Wang Z-X. A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform. Applied Sciences. 2024; 14(11):4756. https://doi.org/10.3390/app14114756
Chicago/Turabian StyleLi, Fan, and Zhong-Xun Wang. 2024. "A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform" Applied Sciences 14, no. 11: 4756. https://doi.org/10.3390/app14114756
APA StyleLi, F., & Wang, Z.-X. (2024). A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform. Applied Sciences, 14(11), 4756. https://doi.org/10.3390/app14114756