Page 694 - Emerging Trends and Innovations in Web-Based Applications and Technologies

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
Data Collection
The success of the Fake Logo Detection System relies heavily on the quality and diversity of the dataset used for training the
model. The first step involves collecting a comprehensive dataset of both real and counterfeit logos from various industries,
such as technology, fashion, food, and entertainment. This diversity ensures that the model can detect fake logos across
multiple sectors.
The dataset will be sourced from online repositories, brand databases, and potentially through partnerships with companies in
brand protection. It will include logos with varying designs, colors, fonts, and orientations, as well as counterfeit logos altered
in common ways, such as modified shapes, resized elements, or distorted text.
To ensure the dataset's quality, images will undergo preprocessing, which includes resizing, normalizing pixel values, and
applying data augmentation techniques like rotation and flipping. These steps help prevent overfitting and ensure the model
can generalize effectively.
In summary, the data collection phase aims to gather a large, diverse set of real and fake logos to train the system effectively,
providing a robust foundation for accurate fake logo detection.

Expected Result
Data Pre-processing
Data pre-processing is a critical step in the development of the Fake Logo Detection System, as it ensures that the input data is
in the right format and quality for training the machine learning model. Proper pre-processing improves the performance and
generalization of the model, helping it to effectively detect fake logos across different variations.
The first step in pre-processing involves resizing the images to a consistent dimension. Since logo images can come in various
sizes, standardizing the image dimensions ensures that the model can process them uniformly. Typically, the images are resized
to a fixed resolution (e.g., 224x224 or 256x256 pixels), which balances computational efficiency and image quality.
Next, pixel normalization is performed to standardize the input data. Pixel values of images typically range from 0 to 255, but
for deep learning models, it is more effective to normalize these values to a range between 0 and 1. This is achieved by dividing
each pixel value by 255, which helps in speeding up the model’s convergence during training and reduces the impact of high-
intensity values on the model’s learning process.
Resizing Images
All images are resized to a consistent resolution (e.g., 224x224 or 256x256 pixels). This ensures that the model receives input
images of the same size, which is crucial for uniform processing and efficient computation during training.
Pixel Normalization
Pixel values of images are normalized by scaling them to a range between 0 and 1. This is achieved by dividing each pixel value
by 255, ensuring that the neural network can process the images more efficiently and helps speed up convergence during
training.
Data To increase the diversity of the training set and prevent overfitting, various data augmentation techniques are applied.
These include:
 Rotation (randomly rotating the logo images)
 Flipping (horizontally or vertically)
 Scaling and zooming
 Random cropping or padding Augmentation helps the model generalize better and recognize logos with different
distortions or orientations.

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