## 3 Experiment
### Experimental Results
#### 3.2.2 Kappa Coefficient  
The Kappa coefficient is computed by utilizing the confusion matrix. Following the computation of the PCE between the noise residual of the image under investigation and the PRNU fingerprint associated with each imaging device, we identify the imaging device corresponding to the highest PCE as the source of said traced image. This iterative process is repeated for all images requiring tracing, resulting in a comprehensive confusion matrix. Finally, Equation (13) is employed to calculate the Kappa value:  
\[\text{Kappa}= \frac{\text{r}-\text{e}}{\text{T}-\text{e}} \tag{13}\]  
where \(r\) represents the count of accurately classified samples, e denotes the expected count of accurately classified samples, and T signifies the total number of samples. The formula for calculating e is as follows:  
\[\text{e}= \sum_{n=1}^{\text{N}}\frac{\sum_{i=1}^{\text{N}}\text{D}(\text{n},i) \sum_{j=1}^{\text{N}}\text{D}(j,\text{n})}{\text{T}} \tag{14}\]  
The Kappa coefficient is a metric utilized for assessing the classifier's accuracy and reflecting its performance. A higher value indicates better agreement between the classification results and actual outcomes. As depicted in Table 1, the InvDN outperforms traditional algorithms (Wavelet, BM3D, and xDrCNN) across all three image resolutions from both the DAXING and DRESDEN datasets. In the case of the DAXING dataset, the InvDN also surpasses the DIDN and MPRNet networks for all three image resolutions, as well as for 128 \(\times\) 128 resolution images in the DRESDEN dataset. However, for 256 \(\times\) 256 and 512 \(\times\) 512 resolution images in the DRESDEN dataset, the InvDN exhibits a slightly inferior performance compared to these two networks. The slightly lower performance of the algorithm in the aforementioned resolutions can be attributed to the utilization of reversible modules within the reversible neural network employed by this algorithm. These modules ensure information consistency during both encoding and decoding processes, thereby guaranteeing the loss-less recovery of information. However, this inevitably introduces additional noise compared to contrast algorithms while maximizing PRNU fingerprint preservation. Furthermore, the algorithm employed in this study utilizes a significantly lower parameter quantity compared to other algorithms of a similar nature, resulting in an efficient PRNU fingerprint extraction performance while conserving memory usage. Thus, its compatibility with small-scale devices enhances its deployability.  
In summary, the proposed algorithm demonstrates an excellent performance across both the smartphone forensic dataset (DAXING) and the digital camera forensic dataset (DRESDEN), irrespective of image resolution. Meanwhile, compared with the contrasting algorithms, this paper's algorithm significantly reduces the number of model parameters,achieving a more lightweight PRNU fingerprint extraction solution, making it easier to deploy on small devices such as mobile phones and remote forensic devices. Experimental results affirming that the PRNU fingerprints extracted by our algorithm are more pristine and dependable.