Deep Learning-Based Image Forgery Detection and Authentication

Author(s)	N. Sivani, P. Sai Kiran, N. Venkatesh, J. Narendra, M. Narasimha Yadav
Country	India
Abstract	Identity verification systems face significant difficulties as a result of face morphing attacks. exploiting the similarity between morphed images to permit fraudulent access as well as the original faces We propose a comprehensive defense against these dangers. Convolutional Neural Network-based learning-based system for detecting face morphing the internet (CNN) This system makes use of the efficient feature extraction and CNNs' pattern recognition capabilities for making precise distinctions between authentic and morphed images of the face. By using a large dataset to train the network, the system learns to recognize subtle artifacts in both real and morphed faces. as well as inconsistencies brought about by the morphing process. The proposed solution offers a reliable and effective strategy for increasing the safety of systems for verifying an individual's identity, ensuring that only genuine access. Significantly, our method demonstrates high detection accuracy. enhancing the security of biometric authentication systems across a variety of applications, such as financial services, access control, and border control.
Keywords	Deep Learning, Convolutional Neural Network (CNN), Morphing.
Discipline	Engineering
Published In	Volume 4, Issue 2, March-April 2026
Published On	2026-04-04

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Advanced International Journal of Multidisciplinary Research