Anchored Privacy Drifting (APD) replaces privacy-sensitive visual elements with semantically equivalent alternatives while anchoring context, evaluated on the new AdaptShield benchmark with reported gains of 10.4% and 8.5% across four MLLM families.
Face de-identification: State-of-the-art methods and comparative studies
2 Pith papers cite this work. Polarity classification is still indexing.
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NullFace performs training-free localized face anonymization by inverting images to noise and denoising with modified identity embeddings from a pre-trained diffusion model.
citing papers explorer
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Seeing Without Exposing: Adaptive Privacy Control for Open-World, Context-Hungry MLLMs
Anchored Privacy Drifting (APD) replaces privacy-sensitive visual elements with semantically equivalent alternatives while anchoring context, evaluated on the new AdaptShield benchmark with reported gains of 10.4% and 8.5% across four MLLM families.
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NullFace: Training-Free Localized Face Anonymization
NullFace performs training-free localized face anonymization by inverting images to noise and denoising with modified identity embeddings from a pre-trained diffusion model.