This project focuses on enhancing digital security through multi-image steganography, leveraging neural networks (NNs). Steganography is the technique of concealing secret data within other non-secret digital media. By utilizing neural networks, this project aims to improve the efficiency and robustness of hiding multiple images within a single cover image.We were able to hide 3 images and extracted all from stego image.
Technologies : Python Libraries like Tensorflow, Keras, Numpy, Pandas etc.
View CodeThis project aims to transform handwritten paragraphs into digital text through Convolutional Neural Networks (CNNs). It starts by preprocessing scanned or photographed handwritten images to improve clarity. Characters are then extracted from these images using different image processing techniques. These isolated characters are fed into a CNN, which recognizes and classifies them based on learned features. The system reconstructs the recognized characters into coherent digital text, facilitating efficient editing, archiving, and processing of handwritten documents. Accuracy we got was 93%.
Technologies : Python Libraries like Tensorflow, Keras, Numpy, Pandas, Pillow, Matplotlib etc.
View CodeCreated an Instagram clone using the MERN (MongoDB, Express.js, React.js, Node.js) stack. Utilized React for an intuitive responsive user interface, Node.js and Express.js for backend operations, and MongoDB for robust data management. Incorporated functionalities including user authentication, creation, updating, and deletion of text and image posts, real-time messaging, follow/unfollow capabilities, and a dynamic feed.
Technologies : Bootstrap (Framework) · Database Design · React.js · Node.js · MongoDB · Express.js
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During my final semester internship at BISAG as a Machine Learning Research Intern, I worked on a project involving Convolutional Neural Networks (CNNs) for image steganography. My project focused on developing advanced techniques to embed and extract hidden information within images using CNNs. This work aimed to enhance the security and efficiency of digital data concealment, leveraging deep learning to improve the accuracy and robustness of steganographic methods.
Duration : December 2023 - April 2024 . 4 months
MTech Computer Science and Engineering
July 2024 - Present
BTech Computer Science and Engineering
2020 - 2024