# An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection
## 1 Introduction  
With the recent increase in the number of online distributions of harmful pornographic contents via new types of personal broadcasting services, a system for automatic detection of online pornographic contents is highly being demanded [1; 2]. Since much of the recent pornographic contents provided through the online personal broadcasting services have harmful scenes either in the form of visual or auditory manner, fast and accurate detection seems vital. However, most previous studies related to the automatic detection of pornographic contents have mainly focused on single modal detection that extracts and uses either visual or auditory features [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14]. One of the limitations of existing methods based on single modal detection approach is that the harmful contents without the detectable elements cannot be detected. In order to reduce these detection errors, several multimodal detection methods have been studied using visual and/or auditory features for detection [15; 16]. Previous studies on the multimodal methods have shown better detection performance than previous single modal methods, even when portions of the harmful visual or auditory elements are absent in the content. However, since the methods proposed in [15; 16] determine the harmfulness only after the harmful contents have completely played, it is difficult to determine the harmfulness of such contents during the early phase of the play if the contents are provided via streaming way on the online media platform. Therefore, there is a need for a detection method that can quickly and accurately detect harmful contents that are played ordistributed online. In particular, a quick detection method that can minimize the detection omissions of harmful contents is needed. Recently, to supplement the problems of the existing studies [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16] and satisfy the requirements of the online harmful content detection, a method that utilizes multiple features extracted from the visual, motion, and auditory elements for the pornographic detections was proposed in [17]. In the detection method proposed in [17], the harmfulness is decided via unit segments that are divided as a particular unit length from the input content. To determine the harmfulness of the online content, the unit segments from an input content are used to classify the harmfulness as quickly as possible. In order to determine the harmfulness of a unit segment, an image descriptor of each video frame, a video segment descriptor of a continuous video frame sequence, a motion descriptor to notate the motion characteristics in the video segment, and an audio descriptor of the unit segment are utilized. These four types of descriptors are extracted from all content segments in the training dataset, and then the independent four component classifiers are developed using each type of the descriptors. This study [17] used the multimodal pornographic detection method with a stacking ensemble approach, where four component classifiers are arranged in a descending order of their performance to improve the recognition performance, especially in terms of the false negative rate, as well as robustness against lack of the visual, motion, or auditory elements in the input content. However, although the visual and auditory elements of the content have characteristics that change over time, because each descriptor used in [17] uses simple static features by averaging the changes in each unit content segment over time, an accurate reflection of these changes over time can be limited. In addition, since three different types of visual features (image descriptor, video descriptor, and motion descriptor) are extracted from the visual elements of the input content, this method leads to a waste of computation time by engaging in three feature extractions independently.