## 4 Results and Discussion
These smaller objects were not correctly identified in the images. Small objects present a greater challenge than larger objects in object-detection tasks [66; 67]. The difficulty in detecting small objects can be attributed to the indistinguishable features, low resolution, complex background, and limited contextual information that they contain [66]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Hat** & **Pin** & **Map** & **Property** & **Screen** & **Member** \\ \hline Accuracy & 0.992 & 0.993 & 0.99 & 0.902 & 0.941 & 0.854 \\ \hline F-score & 0.211 & 0 & 0.087 & 0.813 & 0.213 & 0.804 \\ \hline Weighted F-score & 0.603 & 0.498 & 0.541 & 0.873 & 0.591 & 0.844 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of best CNN models based on testing data in terms of accuracy, F-score, and weighted F-score for each object type (member, screen, property, map, hat, and pin). Figure 10: Number of appearances of each object in the 252,628 images in the organization’s Instagram dataset. Across different architectures, such as multi-scale, single-scale, ResNet, feature pyramid network (FPN), dynamic convolutional network (DCN), and YOLO, smaller objects result in lower average precision [68]. Comparing YOLOv3, Faster R-CNN, and SSD on three large benchmark datasets focusing on small objects, the detection accuracy for small objects was generally low. Faster R-CNN utilizes CNN [66; 69]. According to our research, CNN outperforms YOLO across all objects, which is consistent with these findings. In Figure 11, we present two images that were previously shown in Figure 2. We used our models to classify each image independently. The classifiers correctly identified the image from Figure 2a as a screen, marking a successful detection. Nevertheless, in the second image (Figure 2b), which shows a screen displaying a map, the classifiers recognized the screen but not the map. It would appear that the classifiers have difficulty classifying more complex objects such as maps. The map is less prominent in the image; its colors are bright but not sufficiently distinctive, and it is relatively small in comparison with the rest of the image. It is likely that this combination of factors contributed to the classifiers' inability to accurately detect the map. In order to improve the model's ability to detect small, low-resolution, and unclear objects, as well as to address data imbalance, it may be necessary to increase the training data to include a broader range of examples in different contexts and sizes. Data augmentation may also be beneficial, including rotations, flips, and color adjustments [70]. Moreover, creating synthetic images of rare objects using synthetic data generation techniques can help balance the dataset [71]. While most studies focus on the leakage of text in images [44; 45] and harmful object detection [47; 48; 49], the case study we conducted demonstrates the importance of detecting censored objects in images. The case study described in this paper revealed multiple sensitive objects related to the studied organization on Instagram. These findings demonstrate the vulnerability of the organization to information leakage through images posted on social networks. Such leakage can lead to the unauthorized dissemination of sensitive information, posing a risk to the organization and personal risk to its members. Image censoring is important because members of a targeted organization may not be aware that they have captured sensitive organizational content in their shared images or that combining several objects in the same image could result in a disclosure of classified information. There is a need to raise organizational members' awareness of the risks  
Figure 11: Results of the best classifiers for the two images that were presented in Figure 2. The \(\boxtimes\) symbol indicates that the classifier determined the object is present in the image, while the \(\boxtimes\) symbol indicates that the classifier determined the object is not present in the image.