3 citations · 11 across the 30 of their papers we have counts for
7 papers · 1 filter
When, Where, and Why: Hierarchical Causal Explainability for Video Face Understanding with MLLMs
Anubhav Kataria, Shreya Ghosh, Jingwen Ye +1
Understanding facial behaviour in video requires answering three fundamental questions: when a key facial expression occurs, where the model attends on the face at that moment, and…
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…
DREAMS: Diverse Reactions of Engagement and Attention Mind States Dataset
Monisha Singh, Gulshan Sharma, Ximi Hoque +1
Active attention and engagement are important in improving users' learning experiences. Engagement refers to the level of involvement and interest individuals show towards a partic…
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…
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…
VFace: A Training-Free Approach for Diffusion-Based Video Face Swapping
Sanoojan Baliah, Yohan Abeysinghe, Rusiru Thushara +4
We present a training-free, plug-and-play method, namely VFace, for high-quality face swapping in videos. It can be seamlessly integrated with image-based face swapping approaches…
Investigating the Viability of Employing Multi-modal Large Language Models in the Context of Audio Deepfake Detection
Akanksha Chuchra, Shukesh Reddy, Sudeepta Mishra +2
While Vision-Language Models (VLMs) and Multimodal Large Language Models (MLLMs) have shown strong generalisation in detecting image and video deepfakes, their use for audio deepfa…