# Sensitive Information Detection Based on Deep Learning Models
## 1 Introduction  
The Internet has deeply penetrated people's lives and continues to progress and expand. With the continuous growth of Internet users, the data on the Internet are also showing explosive growth. However, the Internet also contains a large amount of undesirable information, such as pornography, violence, and politically sensitive content, which violates laws and regulations and affects social order and morality. In order to build a healthy and harmonious cyberspace, it is necessary to detect and block such undesirable information. Many experts and scholars have conducted in-depth research on sensitive image recognition, proposing various methods and techniques. These methods and techniques primarily focus on detection and recognition based on image classification, which involves performing multi-layer convolution operations on images to extract features, and then using classifiers to classify images and determine whether they belong to sensitive categories. However, these methods also have some problems and deficiencies, mainly manifested in the following aspects: First, insufficient sensitivity to the size and position of sensitive information in the image. That is, when sensitive information occupies only a small part of the image or is located at the edge or corner of the image, it is difficult to detect. Second, inadequate adaptability to the complexity and diversity of sensitive information in theimage. That is, when sensitive information is mixed or obscured with the background or other objects in the image, it is difficult to recognize. These problems and deficiencies result in reduced accuracy and efficiency of image classification methods for sensitive image recognition, which is very detrimental to the inspection and management of images. In early research, researchers utilized global features of images and classified them using classifiers such as nearest neighbors and support vector machines. By the year 2000, the research focus shifted from global features of images to local features, leading to the emergence of various techniques for local feature extraction. The two main algorithms for feature extraction were the Bag of Visual Words (BOVW) algorithm proposed by [PERSON] and [PERSON] in 2003 [1], and the Scale-Invariant Feature Transform (SIFT) algorithm proposed by [PERSON] in 2004 [2]. Combining local feature extraction algorithms with support vector machine classifiers became the standard approach during this period. [PERSON] et al. [3] proposed a nude image recognition algorithm based on navel and body features, targeting the recognition of nudity composed of the torso, limbs, and face. The algorithm constructs structured feature vectors and then employs a neural network for recognition. [PERSON] et al. [4] utilized a bag-of-visual-words model to create a visual word dictionary, extracting image feature information, and subsequently used an SVM model for the detection of pornographic images. The detection of exposed skin areas is the most primitive method, based on the idea that pornographic images typically contain more exposed skin areas compared to ordinary images. [PERSON] et al. proposed a method for detecting pornographic images based on the YCbCr color model [5], while [PERSON] et al. introduced geometric filters based on fractal dimension and rough text filters to detect skin in images [6]. The AlexNet model was proposed in 2012 and achieved good results on ImageNet in the same year [7]. Since then, most methods proposed have been based on deep convolutional neural networks. In 2015, [PERSON] fine-tuned AlexNet and GoogleNet pretrained on the ImageNet dataset to recognize sensitive images, obtaining a model with recognition accuracy surpassing any other model at that time [8]. With the introduction of two-stage object detection models, such as the RCNN series algorithms [9] and one-stage object detection models like YOLO [10] and SSD [11], many researchers began to incorporate object detection algorithms into their studies. [PERSON] et al.