1. What security features are used in Indian currency?
The Indian currency uses security features such as security thread, intaglio printing (RBI logo), and identification mark. These features are designed to prevent counterfeiting and ensure the authenticity of the currency. However, with advancements in technology, forgers have found ways to overcome these security barriers. To address this issue, image processing algorithms are employed to extract these features, and a mobile app coupled with cloud storage is proposed as a user-friendly and portable solution to detect counterfeit currency.
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2. What CNN model was used for image processing in the literature survey?
In the literature survey, a unique CNN model was used for image processing. The model received an input image scaled down to 80 x 80 pixels. It employed a self-constructed dataset of 40,000 photos, divided into false and true categories. The architecture consisted of convolution layers followed by fully connected layers. The model achieved an 85.6% testing accuracy. Additionally, VGG16 was recommended for counterfeit prediction, and GoogLeNet was employed for enhanced fine-grained picture classification. The DCNN served as a feature extractor, and a multi-class linear SVM was used for final classification.
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3. How does the proposed system detect currency note edges?
In the proposed system, the grayscale image of the currency note is processed through edge detection. This process identifies points in the image with discontinuities or sharp changes in brightness. By detecting edges, the system can effectively segment and extract features from the currency note. This step is crucial in determining the authenticity of the currency note by comparing the intensity of extracted features against the average value. If the intensity of a feature exceeds the average, the currency note is considered real; otherwise, it is deemed fake. Edge detection plays a vital role in the accuracy and reliability of the proposed system for currency note authentication.
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