Image Forgery / Tampering Detection Using Deep Learning and Cloud
Shaikh Misbah Aijaz Ahmed, Dipak Patil
morphed document, Azure form recognizer, CNN, Deep learning, neural network.
Cybercrime has become more prevalent in recent years. With modern photo editing tools as widely available as ever, it has been demonstrated that creating phony papers is incredibly simple [1]. With the help of this tool, which offers tools for doing so, documents can be scanned and forged in minutes. While photo editing software is convenient and widely available, there are also deft methods for investigating these transformed documents. This study presents a framework for investigating digitally modified documents as well as a way of distinguishing between an original document and a digitally morphing document. we created a web application to detect digitally modified photos. This method has more than 95.0 percent accuracy and has proven to be efficient and useful. Recent work on forgery detection using neural networks has proven to be very effective in detecting image forgery additionally we are using Azure Form recognizer service to read data from documents and verify it on the server, this dual approach makes the system robust and very accurate. Deep Learning methods are capable of extracting complex features in an image, resulting in increased accuracy. In contrast to traditional methods of forgery detection, a deep learning model automatically builds the required features, and as a result, it has emerged as a new area of study in image forgery.
Article Details
Unique Paper ID: 155702

Publication Volume & Issue: Volume 9, Issue 1

Page(s): 1484 - 1487
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