Profanity Detection and Removal in Videos using Machine Learning

Chaudhari, Apoorva; Davda, Palak; Dand, Monil; Dholay, Surekha

doi:10.1109/icict50816.2021.9358624

Cited by 6 publications

(2 citation statements)

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“…On the other hand, this part benchmark the current work against previous work that uses ASR systems for the detection of profanities. Recent research proposed a solution for analyzing the video, which helps to identify the profane content through the use of text detection approaches after videos being transcribed by means of ASR systems [70]. The audio samples were extracted from the input video.…”

Section: ) Benchmark Of Asr-based Censorship Systemmentioning

confidence: 99%

“…The total length of test samples was only 1734 second (~ 28.9 minutes). The developed profanity detection using ASR systems and text detection approaches achieved an accuracy of around 85.03% on the reported dataset [70]. The reported ASR-based system containing two stages that are Speech-to-Text phase, and text detection approach, was retrained on the list of profanities proposed in this work to benchmark current work against ASR-based system.…”

Section: ) Benchmark Of Asr-based Censorship Systemmentioning

confidence: 99%

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Deep Learning-Based Detection of Inappropriate Speech Content for Film Censorship

et al. 2022

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Audible content has become an effective tool for shaping one's personality and character due to the ease of accessibility to a huge audible content that could be an independent audio files or an audio of online videos, movies, and television programs. There is a huge necessity to filter inappropriate audible content of the easily accessible videos and films that are likely to contain an inappropriate speech content. With this in view, all the broadcasting and online video/audio platform companies hire a lot of manpower to detect the foul voices prior to censorship. The process has a large cost in terms of manpower, time and financial resources. In addition to inaccurate detection of foul voices due to fatigue of manpower and weakness of human visual and hearing system in long time and monotonous tasks. As such, this paper proposes an intelligent deep learning-based system for film censorship through a fast and accurate detection and localization approach using advanced deep Convolutional Neural Networks (CNNs). The dataset of foul language containing isolated words samples and continuous speech were collected, annotated, processed, and analyzed for the development of automated detection of inappropriate speech content. The results indicated the feasibility of the suggested systems by reporting a high volume of inappropriate spoken terms detection. The proposed system outperformed state-of-the-art baseline algorithms on the novel foul language dataset evaluation metrics in terms of macro average AUC (93.85%), weighted average AUC (94.58%), and all other metrics such as F1-score. Additionally, proposed acoustic system outperformed ASR-based system for profanity detection based on the evaluation metrics including AUC, accuracy, precision, and F1-score. Additionally, proposed system was proven to be faster than human manual screening and detection of audible content for films' censorship.

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Section: ) Benchmark Of Asr-based Censorship Systemmentioning

confidence: 99%

Section: ) Benchmark Of Asr-based Censorship Systemmentioning

confidence: 99%