{"id":"W2161695581","doi":"10.1109/35.883493","title":"Digital rights management and watermarking of multimedia content for m-commerce applications","year":2000,"lang":"en","type":"article","venue":"IEEE Communications Magazine","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":179,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ericsson (Canada)","funders":"","keywords":"Digital watermarking; Digital rights management; Computer science; Watermark; Computer security; Copy protection; Standardization; Copying; Encryption; Cryptography; Digital Watermarking Alliance; The Internet; Telecommunications; Multimedia; World Wide Web; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001184631,0.000117427,0.0001497561,0.0001217092,0.0002555115,0.00007545259,0.001383913,0.00003524074,0.000002851842],"category_scores_gemma":[0.000002250051,0.0001054238,0.00006385633,0.0002207995,0.0001915973,0.0004309566,0.000235997,0.00007753864,0.000008252319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001271319,"about_ca_system_score_gemma":0.000004045233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004143786,"about_ca_topic_score_gemma":0.000004355175,"domain_scores_codex":[0.9992145,0.00002951914,0.0002939959,0.0002112641,0.00009002838,0.0001606662],"domain_scores_gemma":[0.9979861,0.0001512099,0.00008908294,0.001639363,0.00008295648,0.00005128139],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001863817,0.0002930716,0.0005004354,0.00006196795,0.0000627732,7.006404e-7,0.0003469666,0.00001286238,0.000889722,0.04303112,0.0009189566,0.9538628],"study_design_scores_gemma":[0.0008626857,0.00008977902,0.003622954,0.0001059717,0.0000342298,0.00001497377,0.0000153573,0.01773064,0.003979511,0.03674636,0.9364592,0.0003383528],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002340469,0.0004851834,0.9714906,0.001010896,0.00004740987,0.001130898,0.00004379434,0.0003060255,0.02314475],"genre_scores_gemma":[0.5093403,0.0005479705,0.4879561,0.00009100189,0.00001638843,0.0005432211,0.00004449182,0.00000902414,0.001451537],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9535244,"threshold_uncertainty_score":0.4299058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03196347826609724,"score_gpt":0.2741530616674497,"score_spread":0.2421895834013525,"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."}}