🕵️‍♀️ Steganography Detector

Detect the invisible! This project presents a powerful CNN-based steganalysis tool designed to detect hidden data embedded within images, complete with an intuitive Streamlit web application for real-time analysis.
NOTEBOOK: https://www.kaggle.com/code/zobayer0x01/steganography-detector

🌟 Features

• CNN Model: A deep Convolutional Neural Network specifically designed to capture the subtle, high-frequency noise patterns introduced by steganography, enabling robust detection.
• Dataset Handling: Optimized for Kaggle's "StegoImagesDataset" (train/val/test splits with clean/stego subdirectories).
• Class Imbalance: Uses class weighting during training.
• Streamlit Web App: Interactive UI for image upload and prediction.
• Evaluation: Reports accuracy, precision, and recall.

💡 Project Overview

In an era where digital communication is pervasive, steganography poses a subtle threat by allowing malicious data to be concealed within seemingly innocuous images. This project addresses this challenge by training a CNN to rigorously distinguish between original ('clean') and steganographic ('stego') images, providing a crucial tool for digital forensics and security via an intuitive Streamlit interface.

📸 Demo / Screenshots

📊 Dataset

Uses the StegoImagesDataset from Kaggle.

🛠️ Technologies

• Python 3.11
• TensorFlow / Keras
• NumPy, Pillow, Scikit-learn
• Streamlit

📈 Results and Performance

Model performance depends on subtle data patterns. Class weights help balance evaluation. Key metrics: Accuracy, Precision, Recall. Further tuning and advanced architectures can improve results.

🚀 Future Enhancements

• Advanced CNN architectures.
• Specialized data augmentation.
• Detection of diverse steganography methods.
• Explainable AI (XAI) integration.
• Alternative deployment options.

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.