## 2 Related Work
### Sensitive and Harmful Object Detection  
Several studies have investigated the automatic identification of sensitive personal information, such as an ID number or credit card number, in photos. [PERSON] [44] used an object detection model to detect IDs in an image and compared it with other object detection models (SSD, R-FCN) by measuring the mAP. With a mAP of 70.7%, the Faster R-CNN with the ResNet101 model achieved the highest accuracy. [PERSON] and [PERSON] [45] utilized YOLOv3 to detect personally identifiable information. The purpose of these studies was primarily to analyze text extracted from images. Conversely, our research focuses specifically on censored objects within the image itself, which represents a distinct challenge. Several studies have focused on the identification of harmful or sensitive categories of images. [PERSON] et al. [46] presented iPrivacy (version of 22 February 2017), a tool for automatically recommending privacy settings for image sharing based on detecting privacy-sensitive objects. They used a deep multitask learning method to jointly learn representative deep CNNs and discriminative tree classifiers for the accurate detection of privacy-sensitive objects. There have been several studies examining the identification of harmful objects, such as weapons, pornography, and violence. [PERSON] et al. [47] presented an object detection pipeline for detecting guns in images based on Twitter data. Their model was based on the visual representations in vision-language model (VinVL). The model achieved a mean precision of 72.3 on the Twitter Gun dataset, compared with a YOLOv5's performance of 52.6. [PERSON] et al. [48] proposed a multiclassification sensitive-image detection method based on lightweight CNN for detecting photos associated with pornography, politics, and violence. Based on an EfficientNet model, this method combined the Ghost Module idea with a channel attention mechanism for feature extraction training. According to the experimental results on the sensitive-image dataset, the proposed method detected sensitive data with 94.46% accuracy. Finally, in [49], harmful objects were detected using six categories: alcohol, insulting gestures, blood, cigarettes, guns, and knives. The researchers used two baseline object detection architectures: YOLOv5 and Faster R-CNN. It was found that YOLOv5 had the best performance. Unlike previous studies on object detection for sensitive or harmful content, our study examines the specific domain of organizational data leakage using images shared on OSNs, which presents its own set of challenges. With the use of real-world data and deep learning models, we aim to address a critical security concern for organizations in the era of social media and demonstrate the effectiveness of the proposed method in identifying censored objects specific to organizations, highlighting the security risks posed by their leakage and the need for automated solutions to detect and mitigate them. In Table 1, the main studies related to our study are compared. The rows correspond to the studies, while the last row represents our method. Each research work is compared according to several metrics. These metrics include the use of image-based analysis techniques, the use of social media data, and the application of deep learning techniques. In addition, we check for the implementation of image captioning using deep learning methods and the execution of object detection using deep learning methods. Furthermore, we examine the type of detection: sensitive text information, sensitive or censored objects, out-of-context objects, concealed objects, and harmful objects. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \begin{tabular}{c} **Image-Based** \\ **Analysis** \\ \end{tabular}  & \begin{tabular}{c} **Social Media** \\ **Data** \\ \end{tabular}  & \begin{tabular}{c} **Deep Learning** \\ **Techniques** \\ \end{tabular}  & \begin{tabular}{c} **Image Captions** \\ **Using Deep** \\ **Training** \\ \end{tabular}  &