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@article{202014,
        author = {Kanthi and Meghashree and Nikhitha D and Prathiksha and Dr.Babu Rao K},
        title = {A LogoXPose : Unmasking Fake Logos with AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {12698-12705},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202014},
        abstract = {Brand logos are essential for product identification and for maintaining the reputation of businesses. However, the increasing presence of counterfeit products and manipulated digital content has led to the widespread misuse of fake logos. Manually detecting such counterfeit logos is difficult and time-consuming, especially when dealing with large volumes of images and videos on digital platforms. With the advancement of deep learning (DL) techniques such as Convolutional Neural Networks (CNNs), computer systems have achieved significant success in image classification and feature extraction tasks. This paper presents an automated logo authentication system that uses deep learning to detect and classify logos from images and videos as real or fake. The proposed system integrates YOLOv7 for logo detection with a CNN-based classifier for authenticity verification. The system is capable of detecting multiple logos within a single image or video frame and classifying them accurately. Furthermore, the framework provides audio-based verification for recognized brands and generates a detection report for further analysis. By offering a fast and automated method for identifying counterfeit logos in digital media, the proposed approach contributes to improving brand protection.},
        keywords = {Deep Learning, Convolutional Fake Logo Detection, Convolutional Neural Network (CNN), YOLOv7, Deep Learning, Computer Vision, Brand Authentication.},
        month = {May},
        }