FraudSense_AI

"Unmask the Trick, Secure the Treat – Detect Fraud with a Spooky Twist!"

Comment

Inspiration

With online scams and phishing attacks on the rise, I wanted to create a tool that would not only help detect fraud but also engage users with an immersive experience. Inspired by the Halloween season, I envisioned FraudSense_AI as a unique application that merges practical cybersecurity tools with a spooky theme. The goal was to transform a mundane task—fraud detection—into something engaging, educational, and memorable.

What it does

FraudSense_AI allows users to upload an image containing potentially suspicious text, such as SMS messages or email snippets. The app extracts the text, evaluates its legitimacy, and lets users input a URL for additional verification. Through machine learning models, FraudSense_AI provides a confidence score for both the text and URL, displaying the results with a Halloween-themed pie chart and spooky sound effects, making fraud detection both informative and fun.

How I built it

FraudSense_AI is built using Python and Flask for backend functionality, integrating OpenCV and Tesseract OCR for extracting text from images. I used scikit-learn models for text and URL classification, training them on labeled datasets of phishing and legitimate content. The frontend is crafted with HTML and CSS, creating a spooky, engaging user interface with custom animations and Halloween-themed visuals, bringing a unique twist to fraud detection.

Challenges I ran into

Integrating OCR with accurate text extraction from varied image qualities posed an initial challenge, as it required fine-tuning preprocessing steps. Designing a spooky but intuitive interface took time, particularly ensuring that the user experience remained seamless while incorporating themed visuals and sound effects. Handling the classification of URLs accurately also presented challenges due to complexities like web redirections and broken links.

Accomplishments that we're proud of

I’m proud of successfully merging functionality with an engaging UI, making fraud detection more than just a simple task. The Halloween-themed visuals and sound effects work perfectly with the app's purpose, adding a unique twist to a serious topic. I’m also proud of overcoming the technical challenges in OCR and classification to create a working product that provides meaningful results and is both educational and fun.

What I learned

This project deepened my understanding of OCR and image preprocessing, especially the nuances of handling different image types for text extraction. Additionally, designing a themed web interface with spooky effects helped me learn how to enhance user engagement through creative UI design.

What's next for FraudSense_AI

Future plans for FraudSense_AI include refining the machine learning models for better accuracy, especially in classifying complex URLs. Making it accessible as a mobile app could broaden its reach, helping users quickly check suspicious messages and links on the go.

Share this project:

Updates