Rethinking Visual Privacy: A Compositional Privacy Risk Framework for Severity Assessment with VLMs

Existing visual privacy benchmarks largely treat privacy asa binary property, labeling images as private or non-private based onvisible sensitive content. We argue that privacy is fundamentally compo-sitional. Attributes that are benign in isolation may combine to producesevere privacy violations. We introduce the Compositional Privacy RiskTaxonomy (CPRT), a regulation-aware framework that organizes visualattributes according to standalone identifiability and compositional harmpotential. CPRT defines four graded severity levels and is paired with aninterpretable scoring function that assigns continuous privacy severityscores. We further construct a taxonomy-aligned dataset of 6.7K imagesand derive compositional risk scores. By evaluating frontier and open-weight VLMs we find that frontier models align well with compositionalseverity when provided structured guidance, but systematically underes-timate composition-driven risks. Smaller models struggle to internalizegraded privacy reasoning. To bridge this gap, we introduce a deploy-able 8B SFT model that closely matches frontier-level performance oncompositional privacy assessment. Our dataset and models are publiclyavailable at: https://huggingface.co/collections/timtsapras23/cprt.