Paper Title
Feature Extraction using Proxy Content

Abstract
The "Feature Extraction in Proxy Content" project aims to analyze fake news and digitally altered images using machine learning techniques, conducting a thorough analysis of existing research related to fake news detection. This application explores traditional machine learning models to identify the most effective methods for this task. To create a model that classifies fake news as true or false, we employ supervised machine learning algorithms, utilizing tools such as Long Short-Term Memory (LSTM) networks and Python's scikit-learn library. A core feature of the project is the detection of fake Images, defined as digitally altered or manipulated images. A significant limitation of existing fake image detection systems is their ability to detect only specific tampering methods, such as splicing and coloring. To overcome these Challenges, the project leverages the deep learning concept of Convolutional Neural Networks (CNNs) to enhance the detection capabilities of the system. The use of CNNs enables the model to extract intricate features from images, thereby improving accuracy and adaptability across diverse manipulation techniques. This project integrates traditional and deep learning methods to develop a comprehensive framework for detecting both fake news and images. Through a rigorous analysis of proxydata and feature extraction techniques, the model evolves to meet the challenges posed by an ever- evolving digital landscape. Byapplying innovative machine learning algorithms and a user-centric approach, this project aims to support the fight against misinformation and media manipulation across various digital platforms. Keywords- Fake News Detection, LSTM, proxy content, NLP, Python