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---
license: unknown
metrics:
- accuracy
tags:
- art
datasets:
- DataScienceProject/Art_Images_Ai_And_Real_
library_name: keras
language:
- en
pipeline_tag: image-classification
---
# Model Card for Model ID
This model is designed for classifying images as either 'real' or 'fake-Ai generated' using a Convolutional Neural Network (CNN) combined with Error Level Analysis (ELA).
Our goal is to accurately classify the source of the image with at least 85% accuracy and achieve at least 80% in the Recall test.
## Model Details
### Model Description
CNN is a type of deep learning model specifically designed to process and analyze visual data by applying convolutional layers that automatically detect patterns and features in images.
Error Level Analysis (ELA) detects changes in digital images by comparing the differences between the original and a compressed version of the image.
It highlights areas where alterations may have occurred, making it useful for identifying image tampering.
After running ELA we feed the CNN with the result of comparing original and a compressed version of the same image and then we get the output.
### Direct Use
This model can be used to classify images as 'real' or 'fake- Ai generated' based on the presence of anomalies and features characteristic of each category.
### Out-of-Scope Use
The model may not perform well on images outside the scope of art or where the visual characteristics are drastically different from those in the training dataset.
may not detect ai-images from newer diffusion models that has another method of creating ai images.
## Bias, Risks, and Limitations
ELA can be very good in detecting tamparing such as photoshop,
there is many diffusion engines that not tamper the image making the ELA weak against those ai-images.
### Recommendations
Test this model with different hyperparameters / more CNN layers
## How to Get Started with the Model
Prepare Data: Organize your images into appropriate folders and preprocess them by resizing and normalizing.
Train the Model: Use the provided code to train the model on your dataset.
Evaluate: Test the model on a separate set of images to assess performance.
## Training Details
### Training Data
-Dataset: DataScienceProject/Art_Images_Ai_And_Real_
-Preprocessing: Images are resized, image quallity changed , ELA version of this image created.
### Training Procedure
Train the CNN with the Preprocessed images , use valitadion set.
#### Training Hyperparameters
optimizer = RMSprop(lr=0.0005, rho=0.9, epsilon=1e-08, decay=0.0)
epochs = 22
batch_size = 100
loss = "categorical_crossentropy"
metrics=["accuracy"]
early_stopping = EarlyStopping(monitor='val_acc',min_delta=0,patience=2,verbose=0, mode='auto')
## CNN architecture
## Evaluation
The model takes 7-10 minutes to run , based on our dataset , equipped with pc hardware: i9 13900 , 32gb ram , rtx 3080
your mileage may vary.
### Testing Data, Factors & Metrics
precision
recall
f1
confusion_matrix
accuracy
### Results
test accuracy = 0.853
precision = 0.866
recall = 0.8357
f1 = 0.8509
#### Summary
The model preforms well and meet our initial goal ,thus this model can handle the task of image classification real image vs ai generated image. |