Acoustic Pornography Recognition Using Recurrent Neural Network This research proposes a pornography recognition model using audio features utilizing recurrent neural network. The proposed work is totally different from most of the previous attempts on pornography recognition using visual contents of the nudity images or videos. The importance of using sounds for pornography recognition arises in the cases in which the visual features are not adequately informative of the nudity contents such as dark scenes, and scenes with the covered bodies. Acoustic recognition model selects the finest signal representation by feature extraction of MelFrequency Cepstrum Coefficients (MFCCs) after been preprocessed for noise reduction. Extracted features are fed into a network with long shortterm memory (LSTM) cells. The LSTM cells have the capability to solve problems associated with temporal dependencies and require learning longterm and solve the vanishing gradient problems associated with Recurrent Neural Network (RNN). A dataset of pornosound samples has been created based on an existing pornography video dataset. 800 data samples were used including positive and negative samples. The experimental results confirm the feasibility of the proposed acousticdriven approach by demonstrating an accuracy of 86.50%, and Fscore of 86.83%, in the task of pornography recognition.

Local Receptive FieldExtreme Learning Machine based Adult Content Detection Adult contents have become available everywhere whether in social networks, TV channels and websites. Children protection from pornographic contents is required in all societies and environments. Inappropriate visual contents have an impact on childrenu0027s psychological development. Parentsu0027 censorship is important to solve the problem but this task is time consuming and needs full time employment for activities monitoring. Automatic censorship is highly required to be integrated in all types of media such as TV channels. This paper proposes an automated censorship system that is able to be implemented on low cost embedded system. The public dataset NPDI was used to train and evaluate the performance of the proposed deep model for visual contents detection. The key frames selected from videos are used for pornnonporn classification. Efficient features have to be extracted in order to perform accurate classification. The paper utilizes a local Receptive FieldExtreme Learning Machine (LRFELM) model which is a fast deep learning model to extract features and map them to specific classes in a short time. Our proposed method was found to give good performance in terms of accuracy and training time. The accuracy of 82.9% was achieved by using a normal Central Processing Unit (CPU).