Detection of Image Tempering: Conventional and Deep Learning-based Techniques
Keywords:
Image Forgery, Copy-move detection, Deep CNNAbstract
In light of the cumulative use of digital images in a wide range of apps, as well as the accessibility of
image manipulation software, detecting image alteration has become a difficult task. The copy-move
technique is the most commonly used sort of picture counterfeiting. A portion of an image is duplicated
and manipulated in various ways. Handcrafted qualities are commonly used in the identification of
picture forgery and counterfeiting. It has always been a difficulty with earlier photo reproduction
detection systems that they would rather only detect a specific type of tampering if they are aware of
specific image features. Deep learning is currently being used to detect image alteration, which is a
breakthrough. These strategies were even more efficient than prior methods since they were able to
extract complex images from them. The purpose of this work is to teach you about deep learning-based
picture forgery detection methods, why they function, and how they can be improved upon. In addition,
you will learn about publicly available image forgery datasets.
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