In this project, we present a cutting-edge method for Image Forgery Detection using a novel combination of Error Level Analysis (ELA) and Convolutional Neural Networks (CNNs). Our approach stands out for its robustness and high accuracy.

• Novel Approach: Learn how we combine ELA with CNNs, where the output of ELA is used as input for the CNN, enhancing the detection of image forgeries.

• Innovation and Results: Discover the innovative aspects of our method and see how it achieved an impressive accuracy of 90.3% in detecting various types of image forgeries. This accuracy can be fine-tuned to produce even better results.

We used the CASIA 2.0 dataset available on Kaggle because of its reliability, robustness, and versatility in terms of the types of image forgeries present. This is a well-balanced dataset containing over 12,000+ images.

• https://www.kaggle.com/datasets/divg07/casia-20-image-tampering-detection-dataset

1. Error Level Analysis (ELA):

   • ELA reveals differences in compression levels within an image, which can detect edited or manipulated regions.
   • Benefit: Helps detect subtle differences in tampered images that might not be visible to the naked eye.

2. Convolutional Neural Networks (CNNs):

   • CNNs automatically learn visual patterns like textures and discrepancies that might indicate image forgeries.
   • Benefit: Excels at image classification tasks by learning complex features from the input data.

1. Input Image: The input image is processed using Error Level Analysis (ELA) to highlight compression inconsistencies. This gives the CNN model a better idea of which parts of the image are prone to be forged.
2. Convolutional Neural Network (CNN): The ELA output is fed into the CNN, where multiple convolutional layers extract features, and pooling layers reduce dimensions. Dropout is applied to avoid overfitting.
3. Fully Connected Layer: The features from the CNN are passed through dense layers to interpret the image’s forged or authentic characteristics.
4. Output: The system classifies the image as either forged or authentic based on the processed features.

Link to video: https://youtu.be/0iSTFComJ5I

Note: Please be aware that due to some last-minute technical issues, a part of the video has inaudible audio. We apologize for any inconvenience this may cause and appreciate your understanding.

1. Real-Time Forgery Detection:

   • Optimize the system for real-time detection on platforms like social media using faster inference and scalable deployment.

2. Improving Accuracy:

   • Use advanced models like ResNet or Transformers and apply transfer learning to improve detection accuracy.

3. Enhanced Forgery Detection:

   • Integrate techniques for detecting sophisticated forgeries like DeepFakes to increase model robustness.