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@article{181517,
        author = {Vyshnavi Boddu and Kavyasri Mittapalli and Rahul perumandla and Sudheer yeldhandi and B.Dinesh},
        title = {Image forgery detection using CNN},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {4548-4555},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=181517},
        abstract = {With the increasing use of digital images in various applications, the problem of image forgery has become more prevalent than ever. In this paper, we propose a novel image forgery detection system based on Convolutional Neural Networks (CNNs) that can detect various types of image manipulations, including copy-move, splicing, and retouching. Our proposed system integrates Error Level Analysis (ELA) with deep learning techniques to provide a more accurate and reliable solution to the problem of image forgery detection. We evaluated the proposed system on a dataset of real-world images and achieved a high detection accuracy. Our system outperformed existing methods for image forgery detection and demonstrated its potential for various applications, including forensics, security, and digital image analysis. Overall, the proposed CNN-based image forgery detection system offers a robust and effective solution to the growing problem of image manipulation and forgery in today's visual media landscape.},
        keywords = {image forgery detection, digital forensics, machine learning, deep learning, convolutional neural networks},
        month = {June},
        }