6 citations · 7 across the 8 of their papers we have counts for
9 papers
Explainable Deepfake Detection Challenge
Abhijeet Narang, Kartik Kuckreja, Shreya Ghosh +4
Deepfake detection is moving beyond binary classification decisions toward systems that can also explain the visual evidence supporting those decisions. This transition is importan…
XPlainVerse: A Million-Scale Benchmark for Explainable Deepfake Detection
Abhijeet Narang, Kartik Kuckreja, Shreya Ghosh +3
As deepfake detection models increasingly produce natural language explanations, their reasoning often remains weakly grounded in visual artifacts, limiting reliability and user tr…
Objects Before Words: Object-First Inductive Biases for Grounding Language in Child-View Video
Sathira Silva, Abrham Kahsay Gebreselasie, Muhammad Umer Sheikh +3
Learning grounded word meaning from natural experience requires resolving two ambiguities in infant-view recordings: when the named referent appears and where it is in a cluttered…
Pixels Don't Lie (But Your Detector Might): Bootstrapping MLLM-as-a-Judge for Trustworthy Deepfake Detection and Reasoning Supervision
Kartik Kuckreja, Parul Gupta, Muhammad Haris Khan +1
Deepfake detection models often generate natural-language explanations, yet their reasoning is frequently ungrounded in visual evidence, limiting reliability. Existing evaluations…
AV-Deepfake1M++: A Large-Scale Audio-Visual Deepfake Benchmark with Real-World Perturbations
Zhixi Cai, Kartik Kuckreja, Shreya Ghosh +5
The rapid surge of text-to-speech and face-voice reenactment models makes video fabrication easier and highly realistic. To encounter this problem, we require datasets that rich in…
Tell me Habibi, is it Real or Fake?
Kartik Kuckreja, Parul Gupta, Injy Hamed +3
Deepfake generation methods are evolving fast, making fake media harder to detect and raising serious societal concerns. Most deepfake detection and dataset creation research focus…
GEOBench-VLM: Benchmarking Vision-Language Models for Geospatial Tasks
Muhammad Sohail Danish, Muhammad Akhtar Munir, Syed Roshaan Ali Shah +5
While numerous recent benchmarks focus on evaluating generic Vision-Language Models (VLMs), they do not effectively address the specific challenges of geospatial applications. Gene…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
GeoChat: Grounded Large Vision-Language Model for Remote Sensing
Kartik Kuckreja, Muhammad Sohail Danish, Muzammal Naseer +3
Recent advancements in Large Vision-Language Models (VLMs) have shown great promise in natural image domains, allowing users to hold a dialogue about given visual content. However,…