Abstract
The advancement of technology in every aspect of the current age are leading to misuse of data. Therefore, Researchers face the challenging task to identify these manipulated forms of data and distinguish the real data from the forged data. Splicing is one of the most common techniques used for digital image tampering; a selected area copied from the same or another image is pasted in an image. Image forgery detection is considered a reliable way to verify the authenticity of digital images. This method needs to be able to correctly solve several subtasks similar to segmentation, classification, localization. To reduce Human efforts, in this paper will Detect the Forgery part of the Image using Images and CNN, Deep Learning and Python. A variety of tools are frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries. In recent years, convolution neural networks (CNNs) have received much attention, and if has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery either image splicing or copy-move.
Keywords
Image Detection, CNN Classification, Forgery Image, Digital Image, Falsify Images
How to Cite:
Vemula Pavani Siva Prathyusha , Arepalli Rajesh , DETECTION OF FORGERY IMAGE USING CNN , Volume 10 , Issue 4, april 2025, EPRA International Journal of Research & Development (IJRD),