siddhant.

siddhant.

BTech EEE at MIT, Manipal

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Vulnerability Analysis of Deep Learning Model for OCTA Image Classification

Published in AICTC, 2024

The prevalence of Deep Learning in healthcare has revolutionized medical diagnostics, yet the vulnerability of these models to adversarial attacks threatens their security. This study investigates the susceptibility of a sophisticated deep learning model, trained to classify OCTA images as healthy or diabetic, to adversarial perturbations. Despite achieving high accuracy on unperturbed data, the model remains vulnerable to adversarial noise. The study employs techniques like Project Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) to generate adversarial examples and tests their efficacy against the model. Results show that even minor perturbations can lead to misclassification, emphasizing the need for adversarial robustness in healthcare models. As healthcare decisions are critical, incorporating adversarial training is must to mitigate the impact of adversarial vulnerabilities in deep learning-based medical diagnoses.

Adaptive Multi Scale Document Binarisation Using Vision Mamba

Published in Tiny Papers Track at ICVGIP, 2024

Enhancing and preserving the readability of document images, particularly historical ones, is crucial for effective document image analysis. Numerous models have been proposed for this task, including convolutional-based, transformer-based, and hybrid convolutional-transformer architectures. While hybrid models address the limitations of purely convolutional or transformer-based methods, they often suffer from issues like quadratic time complexity. In this work, we propose a Mamba-based architecture for document binarisation, which efficiently handles long sequences by scaling linearly and optimizing memory usage. Additionally, we introduce novel modifications to the skip connections by incorporating Difference of Gaussians (DoG) features, inspired by conventional signal processing techniques. These multiscale high-frequency features enable the model to produce high-quality, detailed outputs.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

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Teaching experience 2

Workshop, University 1, Department, 2015

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