Mixed Real-Life and Anime Harmful Images Classification Using Deep Residual Neural Networks and Migration Learning

Li, Hao; Fan, Chen; Mou, Yi; Li, Yaqin

doi:10.1109/icivc52351.2021.9526941

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2024

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“…In Paper [14], An effort to solve the two common problems in the CNN model is done. That is a decrease in accuracy and overfitting of models.…”

Section: Literature Reviewmentioning

confidence: 99%

Brutality detection and rendering of brutal frames

Madana Mohana,

Singh,

Kumar

et al. 2024

MATEC Web Conf.

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The popularity of anime is increasing exponentially in every part of the world due to its unique storyline, nonstop entertainment, fights, and similar type of content that can hold viewers and keeps them at the edge of their seats. However, with the increase of popularity in anime there has also been an exponential increase in violence and brutality in anime videos. Violent scenes have become much more common in anime videos when compared to generic cinema. This survey paper presents a comprehensive view on the detection of violence in movies and different scenarios using various techniques. Most commonly to automate detection of violence, machine learning is used for training the machine to detect violence. Convolution neural networks (CNN) are used very commonly to understand image pattern recognition with high accuracy. Moreover, use of other different methods such as LSTM and Markov models are also used to detect violence. The main goals kept in mind while working is to detect violence with high accuracy and to use less computation or to perform the action at a high-speed rate.

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“…In Paper [14], An effort to solve the two common problems in the CNN model is done. That is a decrease in accuracy and overfitting of models.…”

Section: Literature Reviewmentioning

confidence: 99%