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Research Paper | Computer Science & Engineering | India | Volume 12 Issue 9, September 2023 | Rating: 5.8 / 10
Detecting and Classifying Inappropriate Content in Youtube Videos Using Deep Learning Approach
Sanaboina Chandra Sekhar, Yandamuri Eswara Anil
Abstract: The proliferation of hate speech, pornography, and violence in online platforms is a significant concern, especially in video content on platforms like YouTube. An automated solution can help identify and remove such content, creating a safer and more positive online environment. The main objective of this project is to identify and classify undesirable content in YouTube videos using a variety of deep learning approaches. The suggested method analyzes video frames and audio segments using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. Creating a dataset of YouTube videos, pre-processing them to extract pertinent visual and audio attributes, and then training the CNN-LSTM model to discover the spatial and temporal relationships between the video frames and audio segments are all steps in the procedure. On a test set of YouTube videos that have been flagged as unsuitable or not by human annotators, Using measurements of accuracy, precision, recall, and F1-score, the model's performance will be evaluated.
Keywords: Detection, Classification, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks
Edition: Volume 12 Issue 9, September 2023,
Pages: 1447 - 1451