arXiv AI
Defending Wearable VLMs Against Private Attribute Inference
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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.
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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.
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Masking Is Not Enough: Generative Restoration for Multimodal De-Identification in Medical AI
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HeteroPROMPT: A Real-time and Privacy-Preserving Heterogeneous Collaborative Perception Framework
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arXiv Machine Learning
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LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection
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arXiv AI
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TRAP: Benchmark for Task-completion and Resistance to Active Privacy-extraction
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