Kartik Narayan

I am a 2nd 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 focuses on computer vision and its applications in face analysis, understanding, and recognition, with a particular emphasis on multimodal LLMs and unified vision models.

Prior to my doctoral studies, I worked as an undergraduate researcher under Prof. Richa Singh and Prof. Mayank Vatsa at the Image Analysis and Biometrics (IAB) Lab, IIT Jodhpur, where I worked on deepfake video generation. During my time at IIT Jodhpur, I also collaborated with Dr. Suman Kundu and Dr. Suchetana Chakraborty. Additionally, I interned at the University of Texas, San Antonio, where I worked with Dr. Heena Rathore and Dr. Faycal Znidi.

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Open to internship opportunities for Summer 2025

My research primarily focuses on computer vision and its applications in face analysis, understanding, and recognition, with the goal of developing robust open-world algorithms that can be deployed for real-world impact. My specific research interests include multimodal LLMs, unified vision models, parameter-efficient fine-tuning, face segmentation, and representation learning. While most of my work has been in the application domain of facial analysis, I am open to exploring general vision research problems, particularly in the field of multimodal LLMs. I have authored multiple first-author papers with publications in top conferences like CVPR.

See my Google Scholar profile for the complete and most recent publications.

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.

FaceXFormer is an end-to-end unified model capable of handling a comprehensive range of facial analysis tasks such as face parsing, landmark detection, head pose estimation, attributes recognition, and estimation of age, gender, race, and landmarks visibility. It leverages a transformer-based encoder-decoder architecture where each task is treated as a learnable token, enabling the integration of multiple tasks within a single framework.

We propose SegFace, a simple and efficient transformer-based model which utilizes learnable class-specific tokens, allowing each token to focus on its corresponding class, thereby enabling independent modeling of each class. It improves the performance of long-tail classes and outperforms previous state-of-the-art models, achieving a mean F1 score of 88.96 (+2.82) on the CelebAMask-HQ dataset and 93.03 (+0.65) on the LaPa dataset.

The proposed PETALface a parameter efficient transfer learning approach adapts to low-resolution datasets beating the performance of pre-trained models with negligible drop in performance on high-resolution and mixed-quality datasets. PETALface enables development of generalized models achieving competitive performance on high-resolution (LFW, CFP-FP, CPLFW, AgeDB, CALFW, CFP-FF) and mixed-quality datasets (IJB-B, IJB-C) with big enhancements in low-quality surveillance quality datasets (TinyFace, BRIAR, IJB-S).

Most prior research in face anti-spoofing (FAS) approaches it as a two-class classification task where models are trained on real samples and known spoof attacks and tested for detection performance on unknown spoof attacks. However, in practice, FAS should be treated as a one-class classification task where, while training, one cannot assume any knowledge regarding the spoof samples a priori. Hyp-OC, is the first work exploring hyperbolic embeddings for one-class face anti-spoofing (OC-FAS).

In this work, we tackle the problem of low-quality face recognition and take a closer look at the nature of data in low resolution datasets and redefine paradigms in terms of model choice, data input pipeline and fine-tuning schemes. With the accumulated effect of all our design choices, we achieve state-of-the-art results in mixed-quality benchmarks (IJB-B, IJB-C) as well as multiple challenging benchmarks for unconstrained face recognition (Tinyface, IJB-S and BRIAR), thereby opening up a new avenue of research in the area.

We propose DeePhyNet, which performs three tasks: it first differentiates between real and fake content; it next determines the signature of the generative algorithm used for deepfake creation to determine which algorithm has been used for generation, and finally, it also predicts the phylogeny of algorithms used for generation. To the best of our knowledge, this is the first algorithm that performs all three tasks together for deepfake media analysis.

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.

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.

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.

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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