• Ms.pavidhraa
• S. Lakshmanan
• A. Mohamed bilal
• N. Mohamed Arshath
• PR. Madhava Krishna

Fake logo identification has become a significant area of research due to the widespread use of logos in a range of digital media and the potential consequences of their misuse. This study proposes an innovative approach to detect bogus logos using Python. Because of the widespread use of logos in a range of digital media and the potential consequences of misusing them, fake logo detection has become a crucial area of research. The method used in advanced image processing techniques, computer vision methods, and deep learning algorithms will accurately identify fraudulent logos in digital photographs. We offer a pipeline that use Convolutional Neural Networks (CNNs) to extract features after performing pre-processing tasks such as image improvement and normalization. A classification algorithm that has been trained on a sizable dataset of real and fraudulent logos is then given these extracted feat sophisticated image processing techniques, and computer vision approaches.

Fake logo detection, Deep learning, Convolutional Neural Networks (CNNs), Image recognition Brand protection, Counterfeit detection, Image processing. Machine learning , Feature extraction, Neural networks, Transfer learning, Data augmentation , Real vs. fake classification.

"Fake logo detection using python [deep learning]", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.h674-h686, June-2024, Available :http://www.jetir.org/papers/JETIR2406772.pdf

"Fake logo detection using python [deep learning]", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pph674-h686, June-2024, Available at : http://www.jetir.org/papers/JETIR2406772.pdf