{"id":"W4323972003","doi":"10.48550/arxiv.2303.05498","title":"Mark My Words: Dangers of Watermarked Images in ImageNet","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Banting and Best Diabetes Centre, University of Toronto; European Commission; Ministry of Science and ICT, South Korea; Berlin Center for Machine Learning; Deutsche Forschungsgemeinschaft; Institute for Information and Communications Technology Promotion; Korea University","keywords":"Computer science; Artificial intelligence; Watermark; Feature (linguistics); Variety (cybernetics); Class (philosophy); Spurious relationship; Extractor; Digital watermarking; Pattern recognition (psychology); Image (mathematics); Machine learning; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003472631,0.0002783688,0.0003770403,0.0006874125,0.00003089383,0.00009738091,0.001611515,0.0002042511,0.00001035093],"category_scores_gemma":[0.00009039199,0.0003237886,0.0001790811,0.001045644,0.000192947,0.0005612001,0.002326224,0.0004000453,0.0001193334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014819,"about_ca_system_score_gemma":0.0001312272,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003032681,"about_ca_topic_score_gemma":0.0001174475,"domain_scores_codex":[0.9981123,0.0001213302,0.0002843822,0.0009653606,0.0001409204,0.0003757067],"domain_scores_gemma":[0.9982183,0.000140048,0.0002512097,0.001168105,0.0001043668,0.0001179579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001202797,0.001720672,0.1108909,0.003984042,0.00148127,0.01904604,0.009472307,0.5667552,0.009000401,0.06647354,0.02798454,0.1819883],"study_design_scores_gemma":[0.004280638,0.0004464085,0.1039765,0.001993481,0.0002304575,0.00003290026,0.0009426985,0.5532647,0.03730775,0.2917197,0.002120569,0.003684178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8375953,0.00004042681,0.1462307,0.0001513036,0.003050514,0.0005699666,0.00005168875,0.0005346233,0.01177555],"genre_scores_gemma":[0.9950693,0.00009527448,0.00167305,0.00002150207,0.00004041124,0.000002040326,0.00001893441,0.00002503861,0.003054426],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2252462,"threshold_uncertainty_score":0.9999214,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05328125916906568,"score_gpt":0.1795337282801119,"score_spread":0.1262524691110463,"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."}}