{"id":"W4411337494","doi":"10.1109/sp61157.2025.00005","title":"UnMarker: A Universal Attack on Defensive Image Watermarking","year":2025,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Digital watermarking; Computer science; Image (mathematics); Computer security; Computer vision; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001145841,0.0006601649,0.0005256597,0.0006651867,0.0005581478,0.001125154,0.0009505521,0.001637136,0.001934624],"category_scores_gemma":[0.004752474,0.0002995418,0.0005863858,0.0003723261,0.002354205,0.002650802,0.002851536,0.001807044,0.0005474404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006462267,"about_ca_system_score_gemma":0.0004339576,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002920921,"about_ca_topic_score_gemma":0.0003799566,"domain_scores_codex":[0.9985083,0.0003716104,0.00006111481,0.000212827,0.0005938799,0.0002523751],"domain_scores_gemma":[0.9965842,0.001295554,0.0003911471,0.001435184,0.0002031395,0.00009076943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007206497,0.0002095631,0.002785065,0.0003589719,0.0001845872,0.0008586425,0.0009833809,0.06367003,0.3460472,0.3029388,0.006054909,0.2751883],"study_design_scores_gemma":[0.0000751246,0.0007142614,0.001280959,0.0001756468,0.00009669703,0.002343936,0.0002316286,0.5216171,0.3461826,0.09970901,0.02745252,0.0001204236],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1374873,0.0009055532,0.8314807,0.001325257,0.0002032998,0.00017645,0.00008388335,0.004247162,0.02409038],"genre_scores_gemma":[0.8977656,0.0002933957,0.09644727,0.0004891509,0.0000385169,0.00005205768,0.00004691712,0.0001642124,0.004702915],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001934624,"threshold_uncertainty_score":0.006471932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01470697924575288,"score_gpt":0.2749178899341233,"score_spread":0.2602109106883704,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}