11 papers
T-IMPACT: A Severity-Aware Benchmark for Contextual Image-Text Manipulation
Gagandeep Singh, Aaditya Yadav, Priyanka Singh
Recent advances in vision-language models and generative editing systems have made it increasingly easy to produce persuasive multimodal misinformation by altering images, text, or…
D-SECURE: Dual-Source Evidence Combination for Unified Reasoning in Misinformation Detection
Samudi Amarasinghe, Gagandeep Singh, Priyanka Singh
Multimodal misinformation increasingly mixes realistic im-age edits with fluent but misleading text, producing persuasive posts that are difficult to verify. Existing systems usual…
Multi-Adapter Representation Interventions via Energy Calibration
Manjiang Yu, Hongji Li, Junwei Chen +4
Representation intervention has emerged as a promising paradigm for aligning large language models toward desired behaviors without modifying model weights. Existing methods typica…
PIXEL: Adaptive Steering Via Position-wise Injection with eXact Estimated Levels under Subspace Calibration
Manjiang Yu, Hongji Li, Priyanka Singh +3
Reliable behavior control is central to deploying large language models (LLMs) on the web. Activation steering offers a tuning-free route to align attributes (e.g., truthfulness) t…
DGM4+: Dataset Extension for Global Scene Inconsistency
Gagandeep Singh, Samudi Amarsinghe, Priyanka Singh +1
The rapid advances in generative models have significantly lowered the barrier to producing convincing multimodal disinformation. Fabricated images and manipulated captions increas…
SGS: Segmentation-Guided Scoring for Global Scene Inconsistencies
Gagandeep Singh, Samudi Amarsinghe, Urawee Thani +3
We extend HAMMER, a state-of-the-art model for multimodal manipulation detection, to handle global scene inconsistencies such as foreground-background (FG-BG) mismatch. While HAMME…