{"id":"W4200483147","doi":"10.1109/ictc52510.2021.9620755","title":"Optimizing Multibit Spread Spectrum Audio Watermarking for Internet of Things","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Information and Communication Technology Convergence (ICTC)","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; Information Technology Research Centre; Ministry of Science and ICT, South Korea; Ministry of Education, Science and Technology; National Research Foundation of Korea","keywords":"Digital watermarking; Discrete cosine transform; Computer science; Robustness (evolution); Watermark; The Internet; Spread spectrum; Discrete wavelet transform; Real-time computing; Computer security; Encryption; Wavelet; Computer network; Algorithm; Wavelet transform; Computer vision; Image (mathematics); Channel (broadcasting)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002929394,0.0001713992,0.0002150129,0.000472627,0.0001524224,0.0001664138,0.001398623,0.0001640654,0.00009766805],"category_scores_gemma":[0.0001199685,0.0001737053,0.00008157897,0.0003538314,0.0001787069,0.001957422,0.0006487902,0.0002695355,0.00001165197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004675821,"about_ca_system_score_gemma":0.00007694733,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001922214,"about_ca_topic_score_gemma":0.00000608101,"domain_scores_codex":[0.9987255,0.00005231638,0.0005760076,0.000239053,0.0002163151,0.0001908072],"domain_scores_gemma":[0.9981079,0.00008158306,0.0004326624,0.0007003501,0.0006347693,0.00004280985],"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":[0.00002516109,0.00003621233,0.0007088607,0.00002443994,0.00004769406,9.681553e-7,0.001528099,0.00001408004,0.001404717,0.9482574,0.0001296588,0.04782264],"study_design_scores_gemma":[0.001184792,0.0002289589,0.0004082365,0.0005994709,0.00002027197,0.00006030165,0.001599766,0.4528942,0.3332818,0.178315,0.03082354,0.0005837087],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006949247,0.0001445574,0.9740438,0.009333974,0.0003457238,0.000259869,0.0000117756,0.0002616458,0.008649363],"genre_scores_gemma":[0.867015,0.001355727,0.1308459,0.0004125221,0.000007483271,0.00008437577,0.00009406698,0.00000527102,0.0001796004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8600658,"threshold_uncertainty_score":0.7083497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02117896023622145,"score_gpt":0.267048763580378,"score_spread":0.2458698033441566,"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."}}