A Hybrid CRNN Model for Multi-Class Violence Detection in Text and Video

• Published in: ITM Web of Conferences Vol. 53; p. 2007
• Main Authors: Ghadekar, Premanand, Agrawal, Kunjal, Bhosale, Adwait, Gadi, Tejas, Deore, Dhananjay, Qazi, Rehanuddin
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Les Ulis EDP Sciences 2023
• Subjects: Artificial neural networks; Deep learning; Gender-based violence; Machine learning; Model accuracy; Neural networks; Recurrent neural networks; Safety; Video data; Violence
• ISSN: 2271-2097; 2431-7578; 2271-2097
• DOI: 10.1051/itmconf/20235302007

Gender-based violence is a critical issue that not only poses a threat to physical safety but also has significant impacts on mental health. Shockingly, up to 1 billion children aged 2-17 years are estimated to have experienced gender-based violence globally, making it a pressing concern for the machine learning and deep learning communities to address. To end this, a novel approach has been proposed in the form of a Convolutional Neural Network and bi-directional LSTM (CRNN) to classify three types of violence present in both text and video data, thereby making the internet a safer space for individuals. The proposed approach utilises two datasets consisting of 400 and 600 samples each for videos and text, respectively, to improve the precision and accuracy of the model. The use of a Convolutional Recurrent Neural Network framework combined with LSTM layers has resulted in an accuracy of 97% on text and 96% on videos, surpassing the performance of existing RNN models. Additionally, the inclusion of dropout and regularizer layers has helped the model avoid overfitting and generalise better on unseen data. Overall, the CRNN-based approach presents a promising solution to the problem of gender-based violence detection, with the potential to significantly improve the safety of individuals online. By leveraging the power of machine learning and deep learning, we can contribute towards creating a safer and more equitable world for all.