{"id":"W2170623172","doi":"10.1109/date.2005.32","title":"A Public-Key Watermarking Technique for IP Designs","year":2005,"lang":"en","type":"article","venue":"Design, Automation, and Test in Europe","topic":"Digital Rights Management and Security","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Concordia University","funders":"","keywords":"Digital watermarking; Robustness (evolution); Computer science; Watermark; Embedding; Digital Watermarking Alliance; Computer security; Key (lock); Finite-state machine; Public-key cryptography; Theoretical computer science; Algorithm; Encryption; 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":[0.0007810786,0.0005284489,0.0002915963,0.0007533632,0.0006657505,0.0007707701,0.000642024,0.0009227765,0.00310373],"category_scores_gemma":[0.004579156,0.0002955621,0.0005423061,0.0005896777,0.001495447,0.002672571,0.001107315,0.001373392,0.0007393619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005990757,"about_ca_system_score_gemma":0.0005535471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001150063,"about_ca_topic_score_gemma":0.0001390426,"domain_scores_codex":[0.9989468,0.0002096689,0.00008187058,0.0001920288,0.0004845311,0.00008503716],"domain_scores_gemma":[0.9977864,0.0006131617,0.0003613327,0.000973044,0.0002263162,0.0000397182],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000364724,0.0001644722,0.0004750687,0.0005028559,0.00005643827,0.0004662545,0.0004522299,0.01906632,0.2697706,0.3914514,0.002219186,0.3150105],"study_design_scores_gemma":[0.0001528764,0.001045184,0.0006296473,0.0002146389,0.0001592956,0.002415183,0.00009972048,0.2015016,0.5711321,0.1493612,0.07316915,0.0001193476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02548997,0.0004719404,0.9629105,0.0004054329,0.0001622951,0.0001401342,0.00004850945,0.001054214,0.009316991],"genre_scores_gemma":[0.6433064,0.0006579777,0.347622,0.0001779956,0.0001234287,0.0002042938,0.00007547489,0.0001109371,0.00772148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00310373,"threshold_uncertainty_score":0.01038301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05993218521477277,"score_gpt":0.2460138049905577,"score_spread":0.1860816197757849,"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."}}