Online Fake Logo Detection System

Every day, hundreds of domain names, websites, and logos are being cloned by cyber criminals who want to gain our trust to steal our data. As a result, faking logos is becoming a big issue in the online world and needs to be addressed. As a result, fake logos on the internet have become a significant source of worry for businesses and consumers. The algorithm can detect differences in logo design, color, and positioning and assess the possibility of a fake logo. The system's accuracy was evaluated on a massive dataset of actual and false logos, and it obtained a high level of accuracy in recognizing fake logos. The fake logo identification technology has the potential to dramatically increase the credibility and dependability of online material, thereby protecting brand identity integrity. This research proposes a method for detecting fake logos using a Context-dependent similarity algorithm. Our approach involves extracting features from the logos and training a machine-learning classifier to distinguish between real and fake logos. We evaluate the performance of our method on a dataset of real and fake logos and demonstrate its effectiveness in detecting fake logos with high accuracy.