EntropyScan detects backdoored LVLMs by quantifying structural anomalies in visual attention distributions on benign samples via Tsallis entropy and reference-anchored Z-score normalization.
National Science Review11(12), nwae403 (2024)
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SpecVQA is a new benchmark dataset and evaluation suite for testing multimodal large language models on scientific spectral image understanding and visual question answering, supported by a curve-preserving sampling method that improves results.
citing papers explorer
-
EntropyScan: Towards Model-level Backdoor Detection in LVLMs via Visual Attention Entropy
EntropyScan detects backdoored LVLMs by quantifying structural anomalies in visual attention distributions on benign samples via Tsallis entropy and reference-anchored Z-score normalization.
-
SpecVQA: A Benchmark for Spectral Understanding and Visual Question Answering in Scientific Images
SpecVQA is a new benchmark dataset and evaluation suite for testing multimodal large language models on scientific spectral image understanding and visual question answering, supported by a curve-preserving sampling method that improves results.