BullyShield

# BullyNet: Deep Learning Framework for Detecting Image-Based Cyberbullying **BullyNet** is a novel deep learning architecture for detecting image-based cyberbullying and technology-facilitated abuse. It is designed to capture the nuanced, multimodal, and adversarial nature of online harassment, and improve robustness and interpretability for real-world deployment. --- ## 📌 Overview Cyberbullying involving images has severe psychological consequences and is difficult to detect due to subtle cues, semantic ambiguity, and contextual dependencies. Traditional methods fall short in tackling this challenge. This repository implements: - **BullyNet**: A hierarchical, dual-context, multimodal neural model that combines lexical, discourse, and graph-based representations.- **Countershame Learning**: A training paradigm that enforces robustness under adversarial paraphrasing and semantic shifts. 🔍 *Application Areas*:- Online safety monitoring- Content moderation for social media- Mental health support tools- Digital ethics research --- ## 🧠 Key Features - ✅ **Contextual Understanding**: Learns abuse patterns from conversation threads, visual cues, and user interaction data.- 🔐 **Adversarial Robustness**: Incorporates adversarial perturbations and semantic alignment techniques.- 🗣 **Interpretability**: Supports attention visualization and latent abstraction modules.- 🔄 **Multi-Stage Architecture**: Includes policy feedback loop and real-time alerting. --- ## 📁 Project Structure ```bash.├── data/ # Preprocessed datasets and loaders├── models/ # BullyNet & Countershame modules├── configs/ # Model and training configuration files├── scripts/ # Training, testing, adversarial evaluation├── utils/ # Helper functions, metrics, logging├── results/ # Output logs, plots, performance reports├── checkpoints/ # Saved models└── README.md # This file