arXiv AI
Defending Wearable VLMs Against Private Attribute Inference
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arXiv AI
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Don't Send What You Don't Need: Question-Guided Token Pruning as a Privacy Defense for Vision-Language Models
arXiv:2609.15671v1 Announce Type: cross Abstract: Visual Question Answering (VQA) with Vision-Language Models (VLMs) is increasingly used in privacy-sensitive and bandwidth-constrained settings. Fede...
arXiv AI
Jun 9
Vision Language Model Helps Private Information De-Identification in Vision Data
arXiv:2606. 09132v1 Announce Type: new Abstract: Visual Language Models (VLMs) have gained significant popularity due to their remarkable ability.
arXiv AI
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Guided Zero-Shot Anomaly Explanation for Anonymized Crowd Video Monitoring: Context, Categorization, and Spatial Localization
The paper introduces XAVAD, a unified approach for explainable anomaly detection in anonymized crowd video monitoring. It combines zero‑shot anonymization, anomaly type categorization, spatial localization, and scene context description using multimodal CLIP embeddings, LLM‑assisted prompting, and a prompt‑guided patching autofocus mechanism. Experiments on public CCTV datasets show XAVAD outperforms state‑of‑the‑art methods and offers insights into privacy‑explainability trade‑offs.
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.
arXiv Computer Vision
Sep 30
From Laboratory to Real World: A New Benchmark Towards Privacy-Preserved Visible-Infrared Person Re-Identification
arXiv:2503.12232v3 Announce Type: replace Abstract: Aiming to match pedestrian images captured under varying lighting conditions, visible-infrared person re-identification (VI-ReID) has drawn intensi...
arXiv Computer Vision
Aug 24
Masking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI
arXiv:2608.21133v1 Announce Type: new Abstract: Medical image-text data can expose protected health information (PHI) through both visible image content as well as accompanying text, creating a barri...