Kartik Narayan

I am a first year Ph.D. student in the Computer Science department at Johns Hopkins University, where I am a member of VIU lab, advised by Dr.Vishal Patel. My research interests broadly lie in the applications of deep learnning in computer vision domain. I work on diffusion models and vision-language models primarily for face biometric applications.

I have previously worked as an undergraduate researcher under Prof. Richa Singh and Prof. Mayank Vatsa at the Image Analysis and Biometrics (IAB) Lab, IIT Jodhpur. I have also interned in University of Texas, San Antonio where I worked with Dr. Heena Rathore and Dr. Faycal Znidi on Epidemic Modelling and Policy Management of COVID-19 using Machine Learning. I also worked with Dr. Suchetana Chakraborty where I used regression algorithms on sensor data to predict human crowd.

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I'm interested in computer vision, deep learning and face biometric. Much of my research is about working with images/videos in the domain of generative modeling and vision-language models.

DF-Platter: Multi-subject Heterogeneous Deepfake Dataset
Kartik Narayan*, Harsh Agarwal*, Kartik Thakral*, Surbhi Mittal*, Mayank Vatsa, Richa Singh

CVPR , 2023
paper / poster

In this research, we emulate the real-world scenario of deepfake generation and spreading, and propose the DF-Platter dataset which contains (i) both low-resolution and high-resolution deepfakes generated using multiple generation techniques, (ii) single-subject and multiple-subject deepfakes. The results demonstrate a significant performance reduction in the deepfake detection task on low-resolution deepfakes and show that the existing techniques fail drastically on multiple-subject deepfakes.

DeSI: Deepfake Source Identifier for Social Media
Kartik Narayan*, Harsh Agarwal*, Surbhi Mittal, Kartik Thakral, Suman Kundu, Mayank Vatsa, Richa Singh

CVPR Workshops, 2022
paper / web-portal / poster / demo video

We develop an algorithm to find the source/propagator of tweets with deepfake/manipulated images/videos relevant to a given text query. The result is shown in form of a force-directed graph which gives temporal insight into the spread pattern and also identifies the volatile nodes in the network by predicting the virality of tweets.

DeePhy: On Deepfake Phylogeny
Kartik Narayan, Harsh Agarwal, Kartik Thakral, Surbhi Mittal, Mayank Vatsa, Richa Singh

International Joint Conferece on Biometrics (IJCB), 2022
paper / database / poster

We proposed the idea of DeepFake Phylogeny and a complementary dataset DeePhy. The paper shows the need to evolve the research of model attribution of deepfakes and facilitates advancements in real life scenarios of plagiarism detection, forgery detection, and reverse engineering of deepfakes.

Using Epidemic Modelling, Machine Learning and Control Feedback Strategy for Policy Management of COVID-19
Kartik Narayan, Heena Rathore, Faycal Znidi

IEEE Access, 2022
paper / code

We propose a threshold mechanism for policy control by analyzing the SIR model and estimating the optimal parameters. Our work helps keep the economic impact of a pandemic under control and also helps in predicting the approximate duration of the lockdwon.

Leveraging ambient sensing for the estimation of curiosity-driven human crowd
Anirban Das, Kartik Narayan, Suchetana Chakraborty

IEEE Systems Conference (SysCon), 2022

We predicted the curious crowd attracted to an object by measuring it's spatiotemporal significance. The work utilizes a set of passive sensors and wireless signal properties for the estimation.

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