👁️‍🗨️ R-SIT - Model for Steganalysis

This repository contains a TensorFlow-trained model saved in saved_model format, designed to detect images manipulated through steganographic techniques. The model uses a Transformer-based architecture and is optimized for grayscale image inputs of size 256x256.

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Model Details

• Expected input: Grayscale image of shape (256, 256, 1)
• Task: Binary classification (stego vs cover)
• Framework: TensorFlow 2.x
• Format: saved_model

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Usage

You can download and load this model in any environment that supports TensorFlow.

Requirements

pip install tensorflow huggingface_hub

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Download and Load

from huggingface_hub import snapshot_download
import tensorflow as tf

# Download the model from Hugging Face Hub
model_path = snapshot_download(repo_id="MarilineDelgado/r-sit")

# Load the model in SavedModel format
model = tf.keras.models.load_model(model_path)

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Example Prediction

import numpy as np

# Test image (1, 256, 256, 1) - grayscale
image = np.random.rand(1, 256, 256, 1).astype(np.float32)

# Make prediction
prediction = model.predict(image)

print("Prediction:", prediction)

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License

This model is licensed under the MIT License. You are free to use and modify it for academic or personal purposes.

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Contact

Created by MarilineDelgado.
For questions or collaboration, please open an issue or contact me through the platform.