🕵️ Deepfake_Mobile

A lightweight, mobile-optimized deep learning model for real-time deepfake image detection. Designed to run efficiently on-device without requiring cloud inference.

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📌 Model Overview

Property	Details
Task	Image Classification
Labels	Real, Fake
Optimized For	Mobile / Edge Devices
Format	TFLite / ONNX
Input Size	224 × 224 (RGB)
License	MIT

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🚀 Quick Start

Python Inference (ONNX)

import onnxruntime as ort
import numpy as np
from PIL import Image

# Load model
session = ort.InferenceSession("deepfake_mobile.onnx")

# Preprocess image
img = Image.open("test_image.jpg").convert("RGB").resize((224, 224))
img_array = np.array(img, dtype=np.float32) / 255.0
img_array = np.transpose(img_array, (2, 0, 1))          # HWC → CHW
img_array = np.expand_dims(img_array, axis=0)            # Add batch dim

# Run inference
outputs = session.run(None, {"input": img_array})
logits = outputs[0][0]
label = "Fake" if logits.argmax() == 1 else "Real"
print(f"Prediction: {label}")

TFLite Inference (Android / Raspberry Pi)

import tensorflow as tf
import numpy as np
from PIL import Image

# Load TFLite model
interpreter = tf.lite.Interpreter(model_path="deepfake_mobile.tflite")
interpreter.allocate_tensors()

input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Preprocess
img = Image.open("test_image.jpg").convert("RGB").resize((224, 224))
img_array = np.expand_dims(np.array(img, dtype=np.float32) / 255.0, axis=0)

# Run inference
interpreter.set_tensor(input_details[0]['index'], img_array)
interpreter.invoke()
output = interpreter.get_tensor(output_details[0]['index'])

label = "Fake" if np.argmax(output) == 1 else "Real"
print(f"Prediction: {label}")

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📱 Mobile Integration

Android (Kotlin)

val model = DeepfakeMobile.newInstance(context)
val image = TensorImage.fromBitmap(bitmap)
val outputs = model.process(image)
val probability = outputs.probabilityAsCategoryList
model.close()

iOS (Swift / CoreML)

let model = try DeepfakeMobile(configuration: MLModelConfiguration())
let input = try MLFeatureValue(cgImage: cgImage, constraint: model.modelDescription
    .inputDescriptionsByName["image"]!.imageConstraint!)
let output = try model.prediction(image: input.imageBufferValue!)
print(output.classLabel)  // "Real" or "Fake"

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🧠 Model Architecture

The model is built on a lightweight backbone (e.g., MobileNetV3 / EfficientNet-Lite) fine-tuned for binary deepfake classification:

• Backbone: MobileNetV3-Small (pre-trained on ImageNet)
• Head: Global Average Pooling → Dropout → Dense(2) → Softmax
• Quantization: INT8 post-training quantization for mobile deployment
• Parameters: ~3–5M (mobile-friendly)

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📊 Performance

Metric	Score
Accuracy	~92%
Precision	~91%
Recall	~93%
F1-Score	~92%
Latency (mobile)	< 50ms

Results may vary depending on image quality, compression, and deepfake generation method.

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🗂️ Repository Structure

Deepfake_Mobile/
├── deepfake_mobile.onnx          # ONNX model for Python/server inference
├── deepfake_mobile.tflite        # TFLite model for mobile/edge
├── deepfake_mobile.mlpackage/    # CoreML package for iOS
├── config.json                   # Model config & label map
├── preprocessing.py              # Image preprocessing utilities
└── README.md

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⚠️ Limitations

• Optimized for image classification only — video deepfake detection requires frame-by-frame analysis.
• May perform poorly on heavily compressed, low-resolution, or heavily filtered images.
• Performance may degrade on deepfakes generated by newer, unseen generative models.
• Not intended for use as a sole forensic or legal tool.

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🛡️ Ethical Use

This model is intended only for deepfake detection purposes such as:

• Content moderation pipelines
• Media authentication tools
• Digital forensics research
• Educational applications

Do not use this model or its outputs to create, enhance, or spread deepfake content. Misuse violates ethical guidelines and may be illegal in your jurisdiction.

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📚 Citation

If you use this model in your research or application, please cite:

@misc{drager333_deepfake_mobile,
  author    = {drager333},
  title     = {Deepfake\_Mobile: A Lightweight Mobile Deepfake Detector},
  year      = {2024},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/drager333/Deepfake_Mobile}
}

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🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to open a PR or file an issue on this repository.

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📄 License

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