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---
license: creativeml-openrail-m
pipeline_tag: image-classification
library_name: transformers
tags:
- deep-fake
- detectioon
---
# **Image-Deep-Fake-Detector**
```
Classification report:
precision recall f1-score support
Real 0.9933 0.9937 0.9935 4761
Fake 0.9937 0.9933 0.9935 4760
accuracy 0.9935 9521
macro avg 0.9935 0.9935 0.9935 9521
weighted avg 0.9935 0.9935 0.9935 9521
```
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.
From the **classification report**, the precision values are:
- **Real:** 0.9933
- **Fake:** 0.9937
- **Macro average:** 0.9935
- **Weighted average:** 0.9935
### 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 Hugging Face write token.
🧪 : https://huggingface.co/spaces/prithivMLmods/convert-to-onnx-dir
### 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. |