This is an implementation of a Python script to detect a series of forgeries that can happen in a document. Our basic module supports
- Signature fraud detection and analysis.
- Copy and move forgery detection.
- Identification document forgery detection.
- Routine document forgery detection and analysis.
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We have tried implementing a cloud-based web application where the user is asked to choose whether to check signature forgery or document forgery.
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Based on his choice he is asked to upload the scanned document. Our machine learning algorithms and neural network-based artificial intelligence detection technique.
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The results generate a graph of the analysis and show the areas where the forgery has been done. It shows the percentage accuracy of the report which is classified as follows:
0% - 10% : Authentic 10% - 55% : Suspicious 55% - 60% : Forged
- The source code of all the Python files used for analysis.
- The backend and frontend code of the web application.
- Several analysis results were generated from the code with the discrepancies underlined.
- Design of the web app.
- Farid, H., 2009. Seeing is not believing. IEEE Spectrum, 46(8).
- Farid, H., 2009. Image forgery detection. IEEE Signal Processing Magazine, 26(2), pp.16-25.
- Johnson, M.K. and Farid, H., 2005, August. Exposing digital forgeries by detecting inconsistencies in lighting. In Proceedings of the 7th workshop on Multimedia and security (pp. 1-10). ACM.