# A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform
## 3 Algorithm Process  
### Zero-Watermarking Generation Algorithm  
Both the zero-watermarking generation process and the subsequent copyright information extraction process inevitably involve a core step, which is the construction of a binary feature image based on SIFT. The pseudocode of the core algorithm in this article is shown in Algorithm 1.  
Figure 3: The fractal structure of chaotic sequences.  
```
Input: F; %Caiiier iamge Output: T; %Binary feature image Initialization: L= zero(92*N); %Sequence matrix Invoke the SIFT algorithm to determine N key points. for(i = 1 to N)do  Compute the SIFT descriptor matrix:S(92*N);  Compute the MrLBP descriptor matrix:M(36*N); for(i = 1 to N)do  Ai0\(\longleftarrow\) sum(M(; i) == 0); Ai1\(\longleftarrow\)sum(M(; i) == 1) ;  Vi = abs(Ai0-Ai1);  L(; i)\(\longleftarrow\)Sort S(; i); 1\(\longleftarrow\)L(; i); %Vector while(Ai0 > Ai1)do  Di = [(92-Vi)/2); %Threshold value for(j = 1 to 92)do if(S(j,i) > Di)then  S(j,i) = 0 else  S(j,i) = 1  end while while(Ai0 \(\leq\) Ai1)do  Di = 1((92 + Vi)/2); for(j = 1 to 92)do if(S(j,i) > Di)then  S(j,i) = 0 else  S(j,i) = 1  end if  end for  T=  vertcat(M,S); return T;
```  
**Algorithm 1**Binary feature generation algorithm based on SIFT  
In this algorithm, F represents the carrier image, T represents the unencrypted binary feature image, N represents the number of feature points, L represents the sequence matrix used to store the sorted SIFT feature descriptor vector group, S represents the SIFT descriptor matrix, M represents the MrLBP descriptor matrix, Vi represents the difference in the number of 0 s and 1 s in M(; i), and Di represents the threshold for binarizing S(; i).  
Combining the pseudocode above, an explanation of the specific steps of the zero-watermark generation algorithm can be provided as follows:  
1. Select the original image requiring copyright protection. Utilize the SIFT 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;
2. 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 SIFT algorithm;
3. 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 SIFT 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.  
The above algorithm steps demonstrate the feature extraction of digital copyright images and the generation of their zero-watermarking images using the SIFT algorithm proposed in this paper, as shown in Figure 4.