1 citations · 2 across the 13 of their papers we have counts for
13 papers
Enhancing Single-Image Facial Demorphing using Multimodal Large Language Models
Nitish Shukla, Arun Ross
Face recognition systems are increasingly vulnerable to morphing attacks, where a composite image is crafted to match multiple identities, enabling unauthorized access and identity…
S2H-DPO: Hardness-Aware Preference Optimization for Vision-Language Models
Nitish Shukla, Surgan Jandial, Arun Ross
Vision-Language Models (VLMs) have demonstrated remarkable progress in single-image understanding, yet effective reasoning across multiple images remains challenging. We identify a…
Facial Demorphing from a Single Morph Using a Latent Conditional GAN
Nitish Shukla, Arun Ross
A morph is created by combining two (or more) face images from two (or more) identities to create a composite image that is highly similar to all constituent identities, allowing t…
Benchmarking Foundation Models for Zero-Shot Biometric Tasks
Redwan Sony, Parisa Farmanifard, Hamzeh Alzwairy +2
The advent of foundation models, particularly Vision-Language Models (VLMs) and Multi-modal Large Language Models (MLLMs), has redefined the frontiers of artificial intelligence, e…
diffDemorph: Extending Reference-Free Demorphing to Unseen Faces
Nitish Shukla, Arun Ross
A face morph is created by combining two face images corresponding to two identities to produce a composite that successfully matches both the constituent identities. Reference-fre…
Metric for Evaluating Performance of Reference-Free Demorphing Methods
Nitish Shukla, Arun Ross
A facial morph is an image created by combining two (or more) face images pertaining to two (or more) distinct identities. Reference-free face demorphing inverts the process and tr…
dc-GAN: Dual-Conditioned GAN for Face Demorphing From a Single Morph
Nitish Shukla, Arun Ross
A facial morph is an image strategically created by combining two face images pertaining to two distinct identities. The goal is to create a face image that can be matched to two d…
Facial Demorphing via Identity Preserving Image Decomposition
Nitish Shukla, Arun Ross
A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby…
SDeMorph: Towards Better Facial De-morphing from Single Morph
Nitish Shukla
Face Recognition Systems (FRS) are vulnerable to morph attacks. A face morph is created by combining multiple identities with the intention to fool FRS and making it match the morp…
Generating Adversarial Attacks in the Latent Space
Nitish Shukla, Sudipta Banerjee
Adversarial attacks in the input (pixel) space typically incorporate noise margins such as or -norm to produce imperceptibly perturbed data that confound deep lea…
Mixed-Type Wafer Classification For Low Memory Devices Using Knowledge Distillation
Nitish Shukla, Anurima Dey, Srivatsan K
Manufacturing wafers is an intricate task involving thousands of steps. Defect Pattern Recognition (DPR) of wafer maps is crucial for determining the root cause of production defec…
Efficient Mixed-Type Wafer Defect Pattern Recognition Using Compact Deformable Convolutional Transformers
Nitish Shukla
Manufacturing wafers is an intricate task involving thousands of steps. Defect Pattern Recognition (DPR) of wafer maps is crucial to find the root cause of the issue and further im…
An Embarrassingly Simple Approach for Wafer Feature Extraction and Defect Pattern Recognition
Nitish Shukla
Identifying defect patterns in a wafer map during manufacturing is crucial to find the root cause of the underlying issue and provides valuable insights on improving yield in the f…