# PornNet: A Unified Deep Architecture for Pornographic Video Recognition
## 1 Introduction  
With the rapid development of the Internet, substantial short videos are uploaded freely onto the Internet by personal users every day. Among these videos publicly available, those with harmful or illegal contents are not only detrimental to personal mental health but also threaten social security and stability [1]. In particular, short pornographic videos seriously affect the mental growth of children and adolescents, since the underage have easy access to these harmful videos with the help of the Internet [2; 3]. Therefore, pornographic video recognition is extremely important for preventing the current Internet environment from being contaminated, and thus plays a crucial role in protecting the mental health of the underage [4]. Although the last two decades have witnessed massive research devoted to recognizing pornographic images [5; 6; 7; 8], pornographic video recognition is still an open problem. In general, the key information contained in the pornographic videos manifests itself in image frames and audio cues. Thus, these two modalities are usually extracted from the videos in the first place, and then handled separately for recognizing pornographic contents. On the one hand, the pornographic images exhibit significant intra-class variances when scenario, scale, and background change. In particular, the private part that distinguishes a pornographic image from normal images often accounts for a small local region, whereas the image background irrelevant to pornographic contents may consist of a large portionof the image area. Previous research [2; 9; 10; 11; 12; 13; 14; 15; 16] focuses on searching a naked person and detecting skin regions by using low-level patterns [17] such as texture, color, and geometrical features, whereas it is unreasonable to assume that all images with large skin areas are pornographic. With the success of deep convolutional neural network (DCNN) in image classification and detection [18], more attention has been paid to the application of DCNN in porn detection [3; 19; 20]. However, distinguishing the difference between sexy photos and porn images is still a challenging task. On the other hand, massive pornography videos can just be accurately recognized by its audio information alone, while some videos with periodic streaming and moaning cannot be understood by image content, since no pornographic images are shown explicitly. Therefore, fusing pornographic audio and visual information is significantly beneficial for improving recognition accuracy. Despite massive efforts devoted to environmental sound classification (ESC) [21; 22; 23; 24; 25] in the audio domain, it is well known that few research focuses on recognizing pornographic audio. The most popular strategy aims to perform the feature embedding obtained by pre-processing an audio signal and subsequently delivers the resulting features to DCNNs. To address the above challenges and difficulties, in this paper, we propose a unified deep architecture termed PornNet in which the two heterogeneous modalities of image and audio in the videos are separately handled for accurate pornographic video recognition. In terms of porn image detection, a detection-classification network (DCNet) based on an anchor free [24] and bidirectional feature pyramid network (BiFPN) [26] is proposed to capture the global and local information of images. More specifically, porn images are classified into three categories according to global information, i.e., normal, sexy, and porn images, whilst six categories which are breast_porn, vagina_porn, penis_porn, buttock_porn, breast_sexy, and buttock_sexy are defined according to local information. The proposed DCNet allows the extraction of discriminative features of normal, sexy, and porn images for both global information and local information from sensitive body parts. On the other hand, DCNN is used for recognizing pornographic audios for the first time.