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@article{177634,
        author = {Muskan Birari and Mukul Gurjar},
        title = {Image Forgery Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2740-2747},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177634},
        abstract = {The fast advancement of digital image editing software has increased the importance of image integrity in journalism, forensic investigations, and cybersecurity. In this study, we present a systematic strategy to detect image manipulation utilizing convolutional neural networks (CNNs) for authentic and forged images. We designed the model to employ VGG16 as a feature extractor with isolated images of ficticiouality and authentic images for training. Deep learning approaches allow us to classify manipulated material and has a testing time of 0.56 seconds per image, which is quite remarkable. Our model has been very effective on measuring copies based on a 92.1 accuracy score using SPLICID while at the same time differentiating manipulated images from authentic. The experimental results indicate the effectiveness of CNN-based forensic counterfeiting detection and the opportunities for consideration and application in digital security and forensic analysis. All data collected was on benchmark datasets and all measured with a score of 100%.},
        keywords = {Image Forgery Detection, Deep Learning, SPLICID, CNN, VGG16, Digital Security and Forensic},
        month = {May},
        }