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• Cyber bullying involvesposting, sharing wrong, private, negative, harmful information about victim. In today's digital world we see many such instances where a particular person is targeted. We are looking for the software solution to curb such bullying/harassment in cyber space. Such solution is expected to
  1. Work onsocial media such as twitter, facebook,etc.;.
  2. Facility toflag and report such incidents to authority.

The Software solution that we propose isCyber Offense Detecting and Reporting (CODAR) Framework, A system that semi-automates the Internet Moderation Process.

• Finds theNSFW composition of a givenYouTube video
• PerformText Toxicity Prediction on publicFacebook Posts/Comments using BeautifulSoup and Facebook API.
• Structures and PerformText Toxicity Prediction onWhatsApp Chat Export Documents.
• Visualise Realtime Toxicity Scored on Tweets using Grafana.
• Chrome Extension to automaticallyblock offensive content
• Reporting Portal for the public to report content.
• Acustom Social Media Platform to test the capablities of this system.

⭐⭐⭐ We have made ourNSFW Image Classification Dataset accessible through Kaggle Link-Sharing and we have used the same. Our classification model for Content Moderation in Social Media Platforms are trained over 330,000 images on a pretrained RESNET50 in five “loosely defined” categories:

1. pornography - Nudes and pornography images
2. hentai - Hentai images, but also includes pornographic drawings
3. sexually_provocative - Sexually explicit images, but not pornography. Think semi-nude photos, playboy, bikini, beach volleyball, etc. Considered acceptable by most public social media platforms.
4. neutral - Safe for work neutral images of everyday things and people.
5. drawing - Safe for work drawings (including anime, safe-manga)

Our text classification BERT model is trained on theJigsaw Toxic Comment Classification Dataset to predict the toxicity of texts to pre-emptively prevent any occurrence of cyberbullying and harassment before they tend to occur. We're choseBERT as to overcome challenges including understanding the context of text so as to detect sarcasm and cultural references, as it uses Stacked Transformer Encoders and Attention Mechanism to understand the relationship between words and sentences, the context from a given sentence.

Text_Input: I want to drug and rape her ======================Toxic: 0.987 Severe_Toxic: 0.053 Obscene: 0.100 Threat 0.745 Insult: 0.124 Identity_Hate: 0.019 ======================Result: Extremely Toxic as classified as Threat, Toxic Action: Text has been blocked.

Confusion Matrix (91% Overall Accuracy)	Finding the NSFW composition of a YouTube video (📷)
Tested on more than 23,000 unseen images.	Downloads the video, Find the NSFW composition using it's Keyframes

Realtime Tweet Toxicity prediction (💬)	Testing the models by integerating with own Social Media Platform (📷+💬)
We love Grafana	Automatically hides NSFW content also shows a disclaimer

Reporting Portal for the public to report content (📷+💬)	Chrome Extension to automatically block offensive content (📷+💬)
The reporting portal with a dashboard to semi-automate the moderation process

• Expand for running CODAR onRaspberry Pi or otherSBCs

  • We'd recommendRaspberry Pi 4 (4GB) runningRaspberry Pi OS Lite andincrease the swap size
  • Follow this to installPyTorch on RPi 4

• Python Compiler (3.7 Recommended)

  • sudo apt updatesudo apt install -y software-properties-commonsudo apt install -y python3 python3-pip

• Necessary Python3 Libraries for CODAR can be installed by running the following command:

  • sudo apt install -y python3-opencvpip install -r Social_Media_Platform/requirements.txtpip install -r Content_Moderation/requirements.txtpip install -r Reporting_Platform/requirements.txt

  • For installation ofPyTorch, refer their official website.
• A MongoDB Server, Grafana Sever, MySQL Server, andaccess to Twitter API
• If you have Docker, you can use the below commands to quickly start a clean MySQL, Grafana and MongoDB Server

  • docker run -d -t -p 27017:27017 --name mongodb mongodocker run --name grafana -d -p 3000:3000 grafana/grafana# Runs MySQL server with port 3306 exposed and root password '0000'docker run --name mysql -e MYSQL_ROOT_PASSWORD="0000" -p 3306:3306 -d mysql

• Add credentails for your MySQL, Twitter API and MongoDB into the Flask Apps. Also, Import our Dashboard JSON into your Grafana Server and configure your data sources accordingly.

Krishnakanth Alagiri	Mahalakshumi V	Vignesh S	Nivetha MK
@bearlike	@mahavisvanathan	@Vignesh0404	@nivethaakm99

• OurNSFW Image Classification Dataset forObscene Image Classification.
• Jigsaw Toxic Comment Classification Dataset for the dataset onText Toxicity Classification
• Hat tip to anyone whose code was used.

MIT ©Axenhammer

Made with ❤️ by Axemhammer