{"id":"W2392401785","doi":"","title":"Digital Image Watermarking Based on Hadamard Transformation and Arnold Transformation","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital watermarking; Hadamard transform; Computer science; Transformation (genetics); Image (mathematics); Computer vision; Domain (mathematical analysis); Artificial intelligence; Digital image; Algorithm; Image processing; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001796148,0.0003487048,0.0004407014,0.0007402075,0.000307291,0.0005881271,0.0003287384,0.0006626125,0.001043817],"category_scores_gemma":[0.000517714,0.0002005258,0.0004163718,0.0007189086,0.0005583482,0.0009101918,0.0003630492,0.0005235179,0.0006145278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001994872,"about_ca_system_score_gemma":0.0003216714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003543112,"about_ca_topic_score_gemma":0.0003374202,"domain_scores_codex":[0.9996843,0.00003953629,0.00001777058,0.00004194744,0.0001912292,0.00002516908],"domain_scores_gemma":[0.9997918,0.00004667812,0.00003302514,0.00003491332,0.00007955496,0.00001410225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002853567,0.0001007637,0.0007439102,0.0003712497,0.00006422905,0.0008446047,0.0001659124,0.01345381,0.5315121,0.04679767,0.001973262,0.4036871],"study_design_scores_gemma":[0.0001797624,0.00147595,0.003373635,0.00008245066,0.0001277665,0.007191742,0.0001186962,0.2882855,0.6016082,0.02257387,0.0747584,0.0002239476],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0433892,0.004129642,0.9398392,0.0003233545,0.0003728838,0.0001408063,0.00006193347,0.001391627,0.01035138],"genre_scores_gemma":[0.5197973,0.004700444,0.4576589,0.0001240562,0.0003127674,0.0001263109,0.0001834435,0.0000568904,0.01703991],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001043817,"threshold_uncertainty_score":0.003491938,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006114544076563362,"score_gpt":0.2293769450587188,"score_spread":0.2232624009821555,"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."}}