{"id":"W2356810396","doi":"","title":"A Novel Image Blind Watermarking Algorithm based on Vector Modulation of DCT Domain","year":2005,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital watermarking; Computer science; Discrete cosine transform; Artificial intelligence; Watermark; Computer vision; Image (mathematics); Algorithm; Pattern recognition (psychology); JPEG; Noise (video); Feature vector; Watermarking attack; Domain (mathematical analysis); Mathematics; Cryptography","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":[0.0002849439,0.0005114356,0.0006473871,0.0008525796,0.0003997684,0.000560911,0.0006530085,0.0008317897,0.001502002],"category_scores_gemma":[0.00060977,0.0002238471,0.0003448843,0.0006293935,0.0004124308,0.001020261,0.0003656073,0.0006100063,0.000959618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003034949,"about_ca_system_score_gemma":0.0004603136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006722257,"about_ca_topic_score_gemma":0.0007763755,"domain_scores_codex":[0.9997336,0.0000286541,0.00001883994,0.00005282311,0.0001445104,0.00002146588],"domain_scores_gemma":[0.999739,0.00004871102,0.00004004383,0.00002909952,0.0001251776,0.00001804466],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003638639,0.00009684228,0.0005484266,0.0002509281,0.00005917609,0.0001631094,0.00007365258,0.01071038,0.4592321,0.01942187,0.003145001,0.5059347],"study_design_scores_gemma":[0.0001817784,0.0008008661,0.001578978,0.00005349206,0.0001061352,0.002210653,0.00003815453,0.5530727,0.3899008,0.006568908,0.04535057,0.0001369072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01101675,0.0009065299,0.9848436,0.0001460715,0.0002552962,0.00009763446,0.00005379791,0.0008635037,0.0018169],"genre_scores_gemma":[0.1533255,0.001185842,0.8336466,0.0001866637,0.00016825,0.0001527933,0.0002223205,0.00006845796,0.01104364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001502002,"threshold_uncertainty_score":0.005024731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01035996638734945,"score_gpt":0.2507873838181687,"score_spread":0.2404274174308192,"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."}}