Page 649 - 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
 Model Structure: The model architecture follows a 3. Recall:
standard CNN design with multiple layers:  Recall measures the proportion of true positive
predictions out of all actual positive cases (genuine
1. Input Layer:
counterfeit logos).
The resized and normalized image is passed into the model
as input.  Formula: Recall=True PositivesTrue Positives+False
Negatives\text{Recall} = \frac{\text{True
2. Convolutional Layers: Positives}}{\text{True Positives} + \text{False
These layers apply filters to the input image to extract Negatives}}Recall=True Positives+False NegativesTrue
essential features such as edges, textures, and patterns that Positives
help distinguish between authentic and fake logos. The
convolution operation applies a filter over the image and 4. F1-Score:
generates feature maps.  The F1-score is the harmonic mean of precision and
recall, providing a single score that balances both
3. Pooling Layers: metrics.
After convolution, pooling (usually max pooling) is used to
down-sample the image and reduce the dimensionality while  Formula: F1-
retaining important features. This also helps make the model Score=2×Precision×RecallPrecision+Recall\text{F1-
more robust to slight changes in the image. Score} = 2 \times \frac{\text{Precision} \times
\text{Recall}}{\text{Precision} + \text{Recall}}F1-
4. Fully Connected Layers:
Score=2×Precision+RecallPrecision×Recall
These are dense layers that connect every neuron to every
other neuron in the next layer. They help the model combine 5. Confusion Matrix:
the extracted features and make a decision about whether  A confusion matrix is used to visualize the performance
the logo is authentic or fake. of the model in terms of true positives, false positives,
true negatives, and false negatives. This matrix helps
5. Output Layer: identify how often the model is making mistakes and in
The final output layer has two nodes: one for authentic logos
what categories.
and one for counterfeit logos. A softmax activation function
is used here to output probabilities, indicating the likelihood 6. ROC Curve and AUC:
that a given image belongs to either of the classes.  The Receiver Operating Characteristic (ROC) curve
plots the true positive rate against the false positive rate
 Loss Function: at various thresholds. The Area Under the Curve (AUC)
For multi-class classification, the categorical cross-entropy provides an aggregate measure of the model’s ability to
loss is used to calculate the difference between the predicted distinguish between classes.
probabilities and the actual class labels.
Result Analysis
 Optimizer: Once the model is trained and tested, its performance will be
The Adam optimizer is commonly used for CNNs because it analyzed by reviewing the following:
adapts the learning rate during training and helps achieve
faster convergence with minimal adjustments to the learning 1. Accuracy Results:
rate.  This will show how effective the model is at classifying
logos correctly. A high accuracy means the model can
Performance Evaluation
successfully identify authentic and counterfeit logos.
After training the model, it's essential to evaluate its
performance. Evaluation metrics help determine how well 2. Error Analysis:
the model generalizes and whether it's suitable for real-  An analysis of the types of errors the model makes. For
world applications. Below are the metrics that will be used instance, whether it is consistently misclassifying logos
for evaluating the performance of the fake logo detection from certain brands or having issues with logos that are
system: very similar in design.
1. Accuracy: 3. False Positive and False Negative Rates:
 Measures the percentage of correct predictions made by  Low false positive and false negative rates indicate a
the model. good model that can accurately differentiate between
fake and real logos.
 Formula: Accuracy=Number of Correct PredictionsTotal
Predictions\text{Accuracy} = \frac{\text{Number of 4. Comparison with Existing Models:
Correct Predictions}}{\text{Total  Results will be compared to existing logo detection
Predictions}}Accuracy=Total PredictionsNumber of systems or traditional methods to determine whether
Correct Predictions the proposed approach performs better in terms of
accuracy, speed, and robustness.
2. Precision:
 Precision measures the proportion of true positive Conclusion
predictions (correctly identified counterfeit logos) out of The proposed Fake Logo Detection System uses AI and web
all predicted positive cases. scraping techniques to automatically detect counterfeit
logos. By leveraging deep learning models, especially
 Formula: Precision=True PositivesTrue Positives+False
Convolutional Neural Networks (CNNs), the system can
Positives\text{Precision} = \frac{\text{True
analyze and classify logos based on their visual features.
Positives}}{\text{True Positives} + \text{False
Through rigorous data pre-processing, such as resizing,
Positives}}Precision=True Positives+False PositivesTrue
normalization, and augmentation, the model can handle a
Positives
wide variety of logo images, even from different sources.
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