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--- |
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license: apache-2.0 |
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base_model: |
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- keras/xception_41_imagenet |
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--- |
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# Model Summary |
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This model is designed for detecting deepfake content in images and video frames. It uses a lightweight Convolutional Neural Network (CNN) trained on the **FaceForensics++ dataset**, focusing on high-resolution face manipulations (c23 compression). The model classifies whether a face in an input image is **real or fake**. |
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* Architecture: CNN-based binary classifier |
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* Input: Aligned and cropped face images (224x224 RGB) |
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* Output: Real or Fake label with confidence |
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* Accuracy: \~92% on unseen FaceForensics++ test set |
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## Usage |
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```python |
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from keras.models import load_model |
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import cv2 |
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import numpy as np |
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model = load_model('deepfake_cnn_model.h5') |
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def preprocess(img_path): |
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img = cv2.imread(img_path) |
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img = cv2.resize(img, (224, 224)) |
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img = img / 255.0 |
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return np.expand_dims(img, axis=0) |
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input_img = preprocess('test_face.jpg') |
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pred = model.predict(input_img) |
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print("Fake" if pred[0][0] > 0.5 else "Real") |
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``` |
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**Input shape**: `(1, 224, 224, 3)` |
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**Output**: Probability of being fake |
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⚠️ *Fails with very low-resolution images or occluded faces.* |
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## System |
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This model is **standalone**, usable in any face verification system or deepfake detection pipeline. Inputs should be properly aligned face crops. Output can be integrated into moderation systems or alerts. |
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**Dependencies**: Keras/TensorFlow, OpenCV for preprocessing |
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## Implementation requirements |
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* Trained on Google Colab with a single NVIDIA T4 GPU |
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* Training time: \~6 hours over 30 epochs |
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* Model inference: <50ms per image |
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* Memory requirement: \~150MB RAM at inference |
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# Model Characteristics |
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## Model initialization |
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The model was **trained from scratch** using CNN layers, ReLU activations, dropout, and batch normalization. |
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## Model stats |
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* Size: \~10MB |
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* Layers: \~8 convolutional layers + dense head |
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* Inference latency: \~40ms on GPU, \~200ms on CPU |
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## Other details |
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* Not pruned or quantized |
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* No use of differential privacy during training |
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# Data Overview |
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## Training data |
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* Dataset: FaceForensics++ (c23 compression level) |
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* Preprocessing: face alignment (using Dlib), resize to 224x224, normalization |
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* Augmentations: horizontal flip, brightness variation |
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## Demographic groups |
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The dataset contains celebrity faces scraped from YouTube. It includes a mix of ethnicities and genders, but **not balanced or labeled** explicitly by demographic. |
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## Evaluation data |
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* Train/Val/Test: 70% / 15% / 15% |
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* The test set includes unseen identities and manipulations (Deepfakes, FaceSwap, NeuralTextures) |
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# Evaluation Results |
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## Summary |
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* Accuracy: \~92% |
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* F1 Score: 0.91 |
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* ROC-AUC: 0.95 |
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## Subgroup evaluation results |
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No explicit subgroup evaluation was conducted, but performance dropped slightly on: |
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* Low-light images |
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* Images with occlusions (masks, hands) |
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## Fairness |
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No explicit fairness metrics were applied due to lack of demographic labels. However, output bias may exist due to uneven representation in training data. |
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## Usage limitations |
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* Struggles on low-res or occluded faces |
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* Doesn’t work on audio-based or voice deepfakes |
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* Requires good lighting and clear facial visibility |
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* Not suitable for legal or forensics-grade use cases without further testing |
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## Ethics |
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This model is intended for **educational and research purposes only**. It should not be used to make real-world judgments (legal, political, etc.) without human oversight. Deepfake detection systems must be transparent about their limitations and avoid misuse in surveillance or personal targeting. |
