Image Forgery Detection Using Machine Learning

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  Fake image for testing

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  Output for the fake image

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 approach stands out for its robustness and high accuracy.

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.

Built With

• deep-learning
• python
• tensorflow