SynthCheck

1. Introduction:

   • This project aims to develop a system capable of detecting synthetic or manipulated images using advanced deep learning techniques, while also providing explanations for model predictions.

2. Objective:

   • Develop a deep learning model to classify images as either real or synthetic based on their visual characteristics.
   • Incorporate explainable AI techniques to provide insights into the model’s decision-making process and highlight features indicative of synthetic images.

3. Dataset Description:

   • Utilized the CIFAKE dataset containing labeled examples of real and synthetic images.
   • Sourced the dataset from Kaggle, specifically curated for synthetic image detection tasks.

4. Data Preprocessing:

   • Preprocessed the image data by resizing, normalizing pixel values, and augmenting to increase dataset diversity.
   • Conducted data augmentation techniques such as rotation, flipping, and cropping to enhance model generalization.

5. Deep Learning Techniques:

   • Implemented convolutional neural networks (CNNs) such as ResNet, VGG, or EfficientNet for image classification tasks.
   • Explored transfer learning and fine-tuning strategies using pre-trained models for improved performance on limited data.

6. Explainable AI (XAI) Techniques:

   • Incorporated techniques such as Grad-CAM, SHAP, or LIME to generate explanations for model predictions.
   • Visualized important regions of input images and highlighted features influencing model decisions, providing transparency and interpretability.

7. Evaluation Metrics:

   • Evaluated model performance using metrics such as accuracy, precision, recall, and F1-score.
   • Assessed the reliability and interpretability of explanations generated by the XAI techniques.

8. Application Scenarios:

   • Explored real-world applications where synthetic image identification can be beneficial, such as forensic analysis, media authenticity verification, and content moderation.
   • Demonstrated how explainable AI techniques provide valuable insights for forensic investigators, journalists, and content moderators.

9. Future Enhancements:

   • Proposed enhancements include integrating multimodal analysis for detecting audio-visual deepfakes, exploring adversarial robustness techniques to improve model reliability, and deploying the system as an API for real-time image verification.

10. Conclusion:

    • Our Synthetic Image Identification Project combines deep learning and explainable AI techniques to detect synthetic images and provide transparent explanations for model predictions.
    • We believe that our system can contribute to combating the spread of misinformation and ensuring the integrity of digital content in various domains.

Demo: