A class project for CSE 509 (Network Security) Fall '23, Stony Brook University
This project delves into the vulnerabilities of NSFW detectors on social media platforms. We employed a systematic black-box attack methodology, leveraging Grad-CAM-generated heatmaps, the study exposes weaknesses in existing detectors, offering insights into the robustness of content moderation systems.
Download/View here
A class project for CSE 577 (Medical Imaging), Spring '23, Stony Brook University
This project explores the Chan-Vese model, initially designed for two-phase segmentation and grayscale images in the 1970s. It highlights the model’s adaptability to handle 3-D images and its extension to multi-phase segmentation, demonstrating its dynamic forces for boundary movement and showcasing its efficacy through practical examples.
Download/View here
Work done as an Intern at SRI International, Summer '23
This study introduces a frequency-based adversarial patch detection method using SAM segmentation and SVM classification on image segments. Through independent analyses of DFT, FFT, and entropy, our approach proves effective in reliably identifying adversarial patches. View the code here
A class project for CS 753 (Automatic Speech Recognition), Fall '19, IIT Bombay
The project aimed to infer a person’s appearance from their voice using a deep neural network trained on YouTube videos. The network encodes speech into a face feature and then decodes it into a canonical face image.
Download/View here
A class project for CS 763 (Computer Vision), Spring '19, IIT Bombay
The presented algorithm revolves around feature-based sparse SLAM, called MonoSLAM, for recovering a camera’s 3D trajectory. Key steps include feature extraction, distance-based feature matching, essential matrix estimation, pose estimation, world coordinate computation, and visualization. Code link here