Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{177779,
        author = {Nikitha Kapparapu and G.Rohitha and Fazal Ur Rahman and K.Kushal},
        title = {Exposing Digital Image Manipulation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2763-2768},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177779},
        abstract = {This paper talks about the increasing sophistication of image manipulation software have made it difficult to distinguish between authentic and altered photographs, requiring new detection methods. Traditional techniques often fall short against advanced fakes, leading researchers to explore Machine Learning (ML) in Artificial Intelligence (AI) for innovative solutions. A promising approach involves segmenting images into smaller regions and analysing them with ML algorithms trained on large datasets of real and altered images. These algorithms detect inconsistencies like pixel intensity and texture patterns, assigning manipulation likelihood scores. Convolutional Neural Networks (CNNs), a deep learning algorithm, are particularly effective in extracting complex visual patterns, making them a significant tool in identifying subtle signs of forgery and counterfeiting techniques. This method identifies subtle signs of manipulation, often undetectable by traditional means. By training on large, diverse datasets, these ML models recognize various counterfeiting techniques, making CNNs a significant advancement in the fight against image forgery.},
        keywords = {Image Forgery Detection, Machine Learning (ML), Deep Learning, Convolutional Neural Networks (CNNs), Image manipulation, Pixel intensity, Visual data analysis.},
        month = {May},
        }