{"passport":{"slug":"visual-foundation-models","display_name":"Visual Foundation Models","model_type":"image","parameter_count":null,"context_window":null,"claimed_developer":"ArXiv AI","confirmed_developer":null,"developer_confirmed_at":null,"developer_source":null,"first_appeared_at":"2026-03-12T12:34:10.161695+00:00","first_appeared_on":"rss_pipeline","status":"confirmed","availability":null,"deployment_status":"not_deployed","availability_scope":null,"gate":1,"eu_ai_act":null,"regulatory_status":{},"open_source":null,"weights_available":null,"license":null,"provenance_chain":[],"parent_model":null,"model_family":null,"superseded_by":null,"supersedes":[],"recommended_replacement":null,"product_links":null},"events":[{"event_type":"first_appearance","event_date":"2026-03-12T12:34:10.161695+00:00","title":"First recorded in AI news","detail":"A paper on an approach to ownership verification of visual foundation models that leverages a small encoder-decoder network to embed digital watermarks into an internal representation of a hold-out set of input images. The method is based on random watermark embedding, which makes the watermark statistics detectable in functional copies of the watermarked model.","source_url":"https://arxiv.org/abs/2603.10695","source_name":"ArXiv AI"}],"delta":{"days_unattributed":119,"claimed_developer":"ArXiv AI","confirmed_developer":null,"match":false}}