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@article{169896,
        author = {Anushka Patil and Lawanya Yellewar and Prachi Mathulkar and Sanika Adgulwar},
        title = {Online fake logo detection system},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {6},
        pages = {3173-3176},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=169896},
        abstract = {Effective techniques to identify and stop the usage of phony logos online are required due to the rise in online fraud and the use of these logos to trick customers. In this research, we will provide a machine learning approach for identifying phony logos. In order to differentiate between authentic and fraudulent logos, our method entails obtaining characteristics from the logos and training a classifier. We will assess our method's performance on a dataset of authentic and fraudulent logos and show that it is capable of accurately identifying false logos. Cybercriminals clone hundreds of domain names, websites, and logos every day in an attempt to win your trust so they can steal your data. It is becoming a big issue in the online world and needs to be addressed.},
        keywords = {Counterfeiting prevention, Fake Logo Detection, Brand protection, Deep Learning, Intellectual property protection, E-commerce security.},
        month = {November},
        }