| --- |
| 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 |
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| 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). |
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| 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. |
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| ### Model Description |
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| 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. |
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| 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. |
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| 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. |
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| ### Direct Use |
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| 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. |
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| ### Out-of-Scope Use |
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| 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. |
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| may not detect ai-images from newer diffusion models that has another method of creating ai images. |
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| ## Bias, Risks, and Limitations |
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| 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. |
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| ### Recommendations |
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| Test this model with different hyperparameters / more CNN layers |
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| ## How to Get Started with the Model |
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| Prepare Data: Organize your images into appropriate folders and run the code |
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| ## Training Details |
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| -Dataset: DataScienceProject/Art_Images_Ai_And_Real_ |
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| -Preprocessing: Images are resized, image quallity changed , ELA version of this image created. |
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| ### Training Procedure |
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| Train the CNN with the Preprocessed images , use valitadion set. |
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| #### Training Hyperparameters |
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| -optimizer = RMSprop(lr=0.0005, rho=0.9, epsilon=1e-08, decay=0.0) |
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| -epochs = 22 |
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| -batch_size = 100 |
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| -loss = "categorical_crossentropy" |
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| -metrics=["accuracy"] |
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| -early_stopping = EarlyStopping(monitor='val_acc',min_delta=0,patience=2,verbose=0, mode='auto') |
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| ## CNN architecture |
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| ## Evaluation |
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| The model takes 7-10 minutes to run , based on our dataset , equipped with the following pc hardware: cpu :i9 13900 ,ram: 32gb , gpu: rtx 3080 |
| your mileage may vary. |
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| ### Testing Data, Factors & Metrics |
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| -precision |
| -recall |
| -f1 |
| -confusion_matrix |
| -accuracy |
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| ### Results |
| -test accuracy = 0.853 |
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| -precision = 0.866 |
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| -recall = 0.8357 |
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| -f1 = 0.8509 |
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| #### Summary |
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| 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. |
