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Fake Image for Testing
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Output of our model
Inspiration
Image Forgery Detection Using Machine Learning
Description:
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 novel approach stands out for its robustness, high accuracy and versatility.
Key Highlights:
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 93.3% in detecting various types of image forgeries.
Research Validation: We also share that our approach has been recognized in the academic community, with our research paper recently being accepted at an international conference.
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.
What it does
How we built it
Challenges we ran into
Accomplishments that we're proud of
What we learned
What's next for Image Forgery Detection Using Machine Learning
Built With
- deep-learning
- machine-learning
- python
- tensorflow
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