# 4 KSecure: A Universal Method for Active Manipulation Detection in Images of Any Resolution
## 3 Method
### Evaluation
#### 3.4.2 Detection Metrics Obtained  
This subsection discusses the metrics used to evaluate the performance of the decoder, i.e., the effectiveness of detecting potential manipulations introduced (or not) into a previously watermarked image. The analysis is divided into two parts: the first concerns binary manipulation detection (i.e., whether an image has been manipulated or not), while the second focuses on the localization of modified regions within the image. Image Manipulation Detection Metrics Obtained  
Figures 8 and 9 present the key binary classification metrics: accuracy and F1 score. These metrics are shown as a function of the resolution of the tested images to observe \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method Name** & **Year** & \begin{tabular}{c} **Dataset Used** \\ **for Testing** \\ \end{tabular} & \begin{tabular}{c} **Cover Size** \\ **(Used in Paper)** \\ \end{tabular} & **SSIM** & **PSNR [dB]** \\ \hline HiDDeN [15] & 2018 & ImageNet & 256 \(\times\) 256 & 0.9234 & 28.87 \\ StegaStamp [17] & 2020 & ImageNet & 400 \(\times\) 400 & 0.930 & 29.88 \\ SteganoCNN [35] & 2020 & ImageNet & 256 \(\times\) 256 & 0.981 & 35.852 \\ HiNet [18] & 2021 & ImageNet & 256 \(\times\) 256 & 0.9920 & 46.88 \\ DKiS [19] & 2024 & ImageNet & 256 \(\times\) 256 & 0.9948 & 41.91 \\ PUSNet [50] & 2024 & ImageNet & 256 \(\times\) 256 & 0.9756 & 38.94 \\ Ours (indirect-resolution) & 2025 & ImageNet & 144 \(\times\) 256 & 0.9995 & 58.9127 \\ Ours (indirect-resolution) & 2025 & Our dataset & 144 \(\times\) 256 & 0.9995 & 58.3834 \\ Ours (indirect-resolution) & 2025 & ImageNet & \
\begin{tabular}{c} Potentially any \\ commercial size \\ (tested up to 1936 \(\times\) 2592) \\ \end{tabular} & 0.9996 & 60.7743 \\ Ours (high-resolution) & 2025 & Our dataset &
\begin{tabular}{c} Potentially any \\ commercial size \\ (tested up to 3840 \(\times\) 2160) \\ \end{tabular} & 0.9996 & 60.2165 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of transparency results obtained using the 4 KSecure algorithm (at full resolution) with other leading methods in the field of data hiding. how detection performance varies across different formats. The graphs display the average values of the selected metrics, calculated for all images of a given size. \[accuracy=\frac{TP+TN}{TP+TN+FP+FN}\]  
\[F1=2\times\frac{Precision\times Recall}{Precision+Recall}\]  
where  
* \(Precision=\frac{TP}{TP+FP}\);
* \(Recall=\frac{TP}{TP+FN}\). Overall, the average accuracy for the described method (for our dataset) was 0.889, while the F1 score reached 0.722. Image Manipulation Detection Metrics Obtained  
This subsection discusses the results generated by the decoder in the context of identifying and localizing potentially modified regions. During testing, the \"attack false\" block was always active, meaning that each image was subjected to additional operations such as JPEG compression or Gaussian blur (see Section 3.3.2). Additionally, all possible combinations of attacks from the \"attack true\" section were considered, allowing for the identification of potential weaknesses in the algorithm and the evaluation of its performance under various types of manipulations. The quality of the predicted manipulated regions was assessed using four metrics: IoU (Intersection over Union)--Figure 10, Dice--Figure 11, Coverage--Figure 12 and AUC (Area Under the Curve)--Figure 13. Below are brief definitions of each metric:  
Figure 8: Average accuracy for different resolutions of test images (our dataset). - theseotpixelsidentifiedbythemodelasmanipulated; \(\odot\)\(G\)
* Dice--similar to IoU, measures the overlap between the predicted manipulation area and the actual manipulation area but places greater emphasis on overall coverage. This formulation gives more weight to the consistency between predictions and the ground truth. The formula is given by \[Dice=2\times\frac{|P\cap G|}{|P\cup G|}\]
* Coverage--measures the fraction of all pixels predicted as \"manipulated\" that actually overlap with the manipulated region in the ground truth.