Project ID 01:
Project Title: A Study on the Integration of Explainable AI and Blockchain and its Application in Credit Scoring
Ashwin Kolhatkar, Final Year BTech (CS)
Manas Ojha, Final Year BTech (CS)
Akash Kademani, Final Year BTech (CS)
Sakshi Kathote, Final Year BTech (CS)
Mihir Pandya, Final Year BTech (CS)
Objectives and Methodology
Credit scores are an important factor that financial institutions consider when deciding whether to approve a loan or not. The scores are designed to predict the likelihood of repayment of a loan. Many of the current ML/AI algorithms just provide the result on whether a particular customer will default on loan repayment or not. An explanation as to why the loan was denied is usually not available. The major issue with complex machine learning models is that they lack interpretability. Our project aims at integration of the Explainable AI (XAI) with the blockchain technology and determining whether a certain user has a good credit score or not, supplemented with an explanation for the same. In this project a system to generate explanations along with the predictions for every single user and making it securely accessible to everyone is designed. Managing the transparency, security and tracking of decisions made by the model over some time for a user is accomplished by constructing a blockchain.
Project ID 02:
Project Title : Generation of Deep Fakes Detection using GANS for textual articles.
Prince Chaudhary, Final Year BTech (IT)
Avani Bajaj, Final Year BTech (IT)
Ajayveer Singh, Final Year BTech (IT)
Vidhi Jain, Final Year BTech (CS)
Objectives and Methodology:
The objective was to create a network that could automatically generate full-length articles and as the fake news publishers have become more sophisticated in their propagation strategies, these machine learning models have been introduced to rapidly detect patterns in news sources and articles. The main task was to provide a classifier that would differentiate between the generated fake text and real text which would help in financial fraud and fake news article detection.
The technology used is Generative Adversarial Networks (GANs). One network, called the generator, generates new data instances, while the other, the discriminator, evaluates them for authenticity; i.e. the discriminator decides whether each instance of data that it reviews belongs to the actual training dataset or not. Basically we have tried to mislead discriminator using two neural networks, pitting one against the other in order to generate new, synthetic instances of data that can pass for real data.
Project ID 03
Project Title : Segmentation of Blood Vessels from Retinal Fundus Images
Student Members :
Akash Dholaria, Final Year BTech (CS)
Gauri Nigam, Final Year BTech (CS)
Shambhavi Shikha Tiwari, Final Year BTech (IT)
Rajat Pandey, Final Year BTech (IT)
Objective and Methodology
The primary objective of this project is image segmentation of Retinal Fundus image in order to obtain a pattern of blood vessels, that can be used to classify disease within a retinal image.
To perform segmentation on the biomedical images, we used semantic segmentation because it is a technique that detects, for each pixel, the object category it belongs to, all labels must be known to the model. In our case, for segmenting an image and classifying it, the input image is a retinal fundus image with the first-hand task of lining and segmenting out the blood vessels pixel by pixel. The architecture used is the U-Net architecture where each pixel is classified to be the part of a vessel or not by predicting a probability and comparing it with a chosen threshold.
Framework of the Methodology
Project ID :04
Project Title: Evaluation of Adversarial training Methods on image based machine learning setups
Vidit Chokshi, Final Year BTech (CS)
Manthansingh Bisht, Final Year BTech (CS)
Vineeth Nair, Final Year BTech (CS)
Abheek Ranjan Das, Final Year BTech (IT)
Security is a mandatory feature in the field of computer vision. Adversarial attacks are one of the most famous techniques used for breaching security, which attempts to fool AI models by malicious input. For our project, we took the case of chest x-rays, since medical data is extremely fragile and minute tampering can lead to unforeseen and disastrous effects. Especially with the increase of data breaches involving medical data, it has become essential to add new types of security measures to make the models robust. In this project, initially, various types of adversarial attacking strategies are used to introduce noise into the images in the input dataset. These modified images are generally indistinguishable to the human eye, but can easily mislead the model into a completely wrong prediction. The goal is to deceive an already trained model. The defensive strategies help to make the model robust by identifying the attacks and avoid misleading predictions by the model. We implemented three different types of defense strategies against poisoning attacks to check the robustness improvement in the model.
Figure 1: Adversarial machine learning project pipeline
Symbiosis Centre For Research and Innovation (SCRI), established in 2009, is the dedicated department of SIU for promoting and facilitating research among students and faculty. Through its academic and administrative services, SCRI enables researchers to achieve excellence in their work, and eventually, translates SIU's vision of creation of knowledge for the benefit of the Society into reality.