# An Enhanced Multimodal Stacking Scheme for Online Pornographic Content Detection
## 2 Literature Review  
Existing multimodal pornographic detection schemes generally use two or more features among the visual features extracted from either a single video frame or a video segment (i.e., a continuous sequence of video frames), the motion-based features, and the acoustic features extracted from the content. Despite the differences in the features used or the combination methods, most multimodal pornography detection methods involve three common steps. The first step extracts the features that each model seeks to use from the corresponding elements of the input content. In the past, low-level features such as skin color, specific female body areas, or distribution of skin pixels were utilized. However, as described in [24], these low-level features do not have sufficient discriminative power to judge the harmful status of the content. Recently, low-level features extracted from the visual and auditory elements have been converted to high-level features by applying the Bag of Word (BoW) or deep learning frameworks [15; 16]. The second step creates an overall classification model via training with the selected appropriate machine learning scheme using the extracted multimodal features to recognize pornographic contents. In general, in the mid-level fusion method approach, all multimodal features can be combined a priori into one representative integrated feature set to develop one classifier, whereas in the late fusion method approach, several component classifiers are made using individualized multimodal features. The late fusion method is the most common method in recent studies [15; 16] because of its superior performance, as described in [15]. In a previous study [25], simple methods with a pre-determined threshold or simple machine learning model such as a decision tree and a naive Bayes were used. In the recent studies of [15; 16], which mainly used the high-level features, the support vector machine (SVM), neural network, and deep learning architectures (which can clearly reflect the non-linearity of the classification hyperspace) were utilized. The final stage is the output engineering step, where all classification results from the component classifiers are integrated for the late fusion method approach. However, in the case of [15], since all of the features were extracted from the visual elements, harmful content detection remained difficult if only the auditory elements of the input content were harmful as existing single modal methods use only the visual elements. In addition, in the case of [16], although the disadvantages of [15] were compensated by using the features extracted from both the visual and auditory elements, since the features extracted from the visual elements are comprised of static features extracted from one still image, the method could not detect the harmful contents well. Moreover, since the methods in [15; 16] require the entire piece of content to determine the harmfulness, it is difficult to quickly detect the harmful contents that are played or distributed online. There is also a problem of omitting certain types of harmful contents in the detection process. In order to resolve the disadvantages of previous studies, the authors in [17] proposed a pornographic video detection method that offers a robust detection performance even if some of the elements used for detection are insufficient. The detection process in [17] is also composed of three steps. In the feature extraction step, four types of descriptors are extracted, including an image descriptor that contains the static features of a video frame, a video segment descriptor containing the static features of a video segment, a motion descriptor representing the motion features in a video segment, and an audio descriptor that contains the static features of an audio segment. These descriptors are extracted by dividing the input content into 10-s unit content segments. Each segment is judged for its harmfulness instead of using the entire piece of the content for early detection.