prithivMLmods
/

Deep-Fake-Detector-Model

@@ -6,41 +6,108 @@ tags:
 - deep-fake
 - detectioon
 ---
-![pipeline](dfd.jpg)
-# **Image-Deep-Fake-Detector**
-The **precision score** is a key metric to evaluate the performance of a deep fake detector. Precision is defined as:
-\[
-\text{Precision} = \frac{\text{True Positives}}{\text{True Positives + False Positives}}
-\]
-It indicates how well the model avoids false positives, which in the context of a deep fake detector means it measures how often the "Fake" label is correctly identified without mistakenly classifying real content as fake.
-# Demo Inference:
-![Screenshot 2025-01-27 at 19-54-50 prithivMLmods_Deep-Fake-Detector-Model · Hugging Face.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/oWLVwLcXAP19uvCd8iqZQ.png)
-### Key Observations:
-1. **High precision (0.9933 for Real, 0.9937 for Fake):**
-   The model rarely misclassifies real content as fake and vice versa. This is critical for applications like deep fake detection, where false accusations (false positives) can have significant consequences.
-2. **Macro and Weighted Averages (0.9935):**
-   The precision is evenly high across both classes, which shows that the model is well-balanced in its performance for detecting both real and fake content.
-3. **Reliability of Predictions:**
-   With precision near 1.0, when the model predicts a video as fake (or real), it's highly likely to be correct. This is essential in reducing unnecessary manual verification in real-world applications like social media content moderation or fraud detection.
-### ONNX Exchange
-The ONNX model is converted using the following method, which directly writes the ONNX files to the repository using the HF中国镜像站 write token.
-🧪 : https://huggingface.co/spaces/prithivMLmods/convert-to-onnx-dir
-![Screenshot 2025-01-27 at 19-03-01 ONNX - a HF中国镜像站 Space by prithivMLmods.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/5T979tVYJ4jCKzlE6nOma.png)
-### Conclusion:
-The deep fake detector model demonstrates **excellent precision** for both the "Real" and "Fake" classes, indicating a highly reliable detection system with minimal false positives. Combined with similarly high recall and F1-score, the overall accuracy (99.35%) reflects that this is a robust and trustworthy model for identifying deep fakes.

 - deep-fake
 - detectioon
 ---
+# **Deep-Fake-Detector-Model**
+# **Overview**
+The **Deep-Fake-Detector-Model** is a state-of-the-art deep learning model designed to detect deepfake images. It leverages the **Vision Transformer (ViT)** architecture, specifically the `google/vit-base-patch16-224-in21k` model, fine-tuned on a dataset of real and deepfake images. The model is trained to classify images as either "Real" or "Fake" with high accuracy, making it a powerful tool for detecting manipulated media.
+### **Key Features**
+- **Architecture**: Vision Transformer (ViT) - `google/vit-base-patch16-224-in21k`.
+- **Input**: RGB images resized to 224x224 pixels.
+- **Output**: Binary classification ("Real" or "Fake").
+- **Training Dataset**: A curated dataset of real and deepfake images (e.g., `Hemg/deepfake-and-real-images`).
+- **Fine-Tuning**: The model is fine-tuned using HF中国镜像站's `Trainer` API with advanced data augmentation techniques.
+- **Performance**: Achieves high accuracy and F1 score on validation and test datasets.
+# **Model Architecture**
+The model is based on the **Vision Transformer (ViT)**, which treats images as sequences of patches and applies a transformer encoder to learn spatial relationships. Key components include:
+- **Patch Embedding**: Divides the input image into fixed-size patches (16x16 pixels).
+- **Transformer Encoder**: Processes patch embeddings using multi-head self-attention mechanisms.
+- **Classification Head**: A fully connected layer for binary classification.
+# **Training Details**
+- **Optimizer**: AdamW with a learning rate of `1e-6`.
+- **Batch Size**: 32 for training, 8 for evaluation.
+- **Epochs**: 2.
+- **Data Augmentation**:
+  - Random rotation (±90 degrees).
+  - Random sharpness adjustment.
+  - Random resizing and cropping.
+- **Loss Function**: Cross-Entropy Loss.
+- **Evaluation Metrics**: Accuracy, F1 Score, and Confusion Matrix.
+# **Inference with HF中国镜像站 Pipeline**
+```python
+from transformers import pipeline
+# Load the model
+pipe = pipeline('image-classification', model="Deep-Fake-Detector-Model", device=0)
+# Predict on an image
+result = pipe("path_to_image.jpg")
+print(result)
+```
+#### **Inference with PyTorch**
+```python
+from transformers import ViTForImageClassification, ViTImageProcessor
+from PIL import Image
+import torch
+# Load the model and processor
+model = ViTForImageClassification.from_pretrained("Deep-Fake-Detector-Model")
+processor = ViTImageProcessor.from_pretrained("Deep-Fake-Detector-Model")
+# Load and preprocess the image
+image = Image.open("path_to_image.jpg").convert("RGB")
+inputs = processor(images=image, return_tensors="pt")
+# Perform inference
+with torch.no_grad():
+    outputs = model(**inputs)
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=1).item()
+# Map class index to label
+label = model.config.id2label[predicted_class]
+print(f"Predicted Label: {label}")
+```
+# **Performance Metrics**
+- **Accuracy**: ~95% on the test set.
+- **F1 Score**: ~94% (macro-average).
+- **Confusion Matrix**:
+  ```
+  [[True Positives, False Negatives],
+   [False Positives, True Negatives]]
+  ```
+# **Dataset**
+The model is fine-tuned on the `Hemg/deepfake-and-real-images` dataset, which contains:
+- **Real Images**: Authentic images of human faces.
+- **Fake Images**: Deepfake images generated using advanced AI techniques.
+# **Limitations**
+The model is trained on a specific dataset and may not generalize well to other deepfake datasets or domains.
+- Performance may degrade on low-resolution or heavily compressed images.
+- The model is designed for image classification and does not detect deepfake videos directly.
+# **Ethical Considerations**
+**Misuse**: This model should not be used for malicious purposes, such as creating or spreading deepfakes.
+**Bias**: The model may inherit biases from the training dataset. Care should be taken to ensure fairness and inclusivity.
+**Transparency**: Users should be informed when deepfake detection tools are used to analyze their content.
+# **Future Work**
+- Extend the model to detect deepfake videos.
+- Improve generalization by training on larger and more diverse datasets.
+- Incorporate explainability techniques to provide insights into model predictions.
+# **Citation **
+```bibtex
+@misc{Deep-Fake-Detector-Model,
+  author = {prithivMLmods},
+  title = {Deep-Fake-Detector-Model},
+  initial = {2024},
+  last_updated = {31 Jan 2025}
+}