arXiv Computer Vision
Oct 16

AI Knows Where You Are: Exposure, Bias, and Inference in Multimodal Geolocation with KoreaGEO

The paper introduces KoreaGEO, a fine‑grained, multimodal, privacy‑aware benchmark for image‑based geolocation built from Korean street views. It covers four socio‑spatial clusters, nine place types, and includes two captioning styles that mimic real‑world privacy exposure. Using a three‑path protocol (image‑only, functional‑caption, high‑risk‑caption), the study shows that input modality has a stronger effect on localization accuracy and privacy risk than model size or architecture, and reveals structural prediction biases toward core cities.

By Xiaonan Wang, Bo Shao, Hansaem Kim
arXiv Machine Learning
Aug 20

On the Robustness of Vision-Language Models in Zero-shot Privacy Classification

The paper investigates whether large, instruction‑following Vision‑Language Models (VLMs) can reliably perform zero‑shot image privacy classification. It compares three open‑source VLMs to specialized privacy models on two public benchmarks, evaluating accuracy, robustness to image degradations (compression, lighting changes, noise), inference speed, and parameter count. The findings show that while VLMs remain robust to perturbations, they are less accurate and significantly slower than smaller, purpose‑built privacy models, indicating that scaling alone does not guarantee effective privacy classification.

By Alina Elena Baia, Alessio Xompero, Andrea Cavallaro