{"id":"W3133028619","doi":"10.1109/icmla51294.2020.00172","title":"Image Watermarking with Region of Interest Determination Using Deep Neural Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Digital watermarking; Robustness (evolution); Computer science; Transparency (behavior); Artificial intelligence; Watermark; Embedding; Artificial neural network; Discrete cosine transform; Computer vision; Deep neural networks; Image (mathematics); Pattern recognition (psychology); Computer security","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":[0.0005052557,0.0006268313,0.0005553084,0.0006626084,0.0002406229,0.0005368215,0.0007937234,0.0007784349,0.000892346],"category_scores_gemma":[0.001378021,0.0003297878,0.0005105296,0.000531166,0.0004460517,0.001042348,0.000820803,0.0007427304,0.0003735683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005807935,"about_ca_system_score_gemma":0.0004597753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001770766,"about_ca_topic_score_gemma":0.002341931,"domain_scores_codex":[0.9997651,0.00003274783,0.00001609289,0.00006706358,0.00008585337,0.00003319457],"domain_scores_gemma":[0.9995635,0.000119268,0.0001192523,0.00007157079,0.0001081483,0.00001810432],"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":[0.0005146068,0.0001486025,0.00104442,0.0001421527,0.0001023683,0.0002098578,0.00007166331,0.1716644,0.1784999,0.004368159,0.001785231,0.6414487],"study_design_scores_gemma":[0.00001151806,0.00007304946,0.0003637054,0.00001058478,0.00002212515,0.0001160483,0.00000949381,0.9485427,0.04808867,0.001644381,0.001103594,0.00001412176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05171119,0.0009999011,0.9434227,0.0001843042,0.00008080047,0.00006690811,0.00008512218,0.001796477,0.00165258],"genre_scores_gemma":[0.6122369,0.0008675103,0.3805591,0.0002022466,0.00007513295,0.0001076706,0.0003338698,0.0001179366,0.005499654],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001770766,"threshold_uncertainty_score":0.004213929,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04776475278249906,"score_gpt":0.2614722999997102,"score_spread":0.2137075472172111,"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."}}