{"id":"W2494067876","doi":"10.1109/aiccsa.2015.7507100","title":"AES cryptography in color image steganography by genetic algorithms","year":2015,"lang":"en","type":"article","venue":"","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Steganography; Computer science; Information hiding; Cryptography; Steganography tools; Least significant bit; Cover (algebra); Image (mathematics); Genetic algorithm; Grayscale; Artificial intelligence; Digital watermarking; Computer vision; Algorithm; Theoretical computer science; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003117867,0.0002379677,0.0002339096,0.0006342207,0.00009246229,0.0002992785,0.001114637,0.0001040383,0.00000438027],"category_scores_gemma":[0.00001294918,0.00020694,0.0001349282,0.001807944,0.0002015343,0.0009056743,0.0002440306,0.0001810617,0.00001141838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002170371,"about_ca_system_score_gemma":0.00003994676,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006264501,"about_ca_topic_score_gemma":0.00002635724,"domain_scores_codex":[0.9981919,0.0001007517,0.0003276342,0.0005469493,0.0003472267,0.000485533],"domain_scores_gemma":[0.9988786,0.00004609343,0.00008023135,0.0006624443,0.0001210322,0.0002115802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002299865,0.002572004,0.2208021,0.0001352121,0.0002439016,0.000724403,0.009204733,0.0001513396,0.03031927,0.1652851,0.2124092,0.3579227],"study_design_scores_gemma":[0.00501719,0.002198396,0.01603097,0.0001519604,0.00002925351,0.0001211138,0.0005529004,0.03072914,0.1358198,0.6257075,0.1803989,0.003242851],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03868625,0.0005869666,0.9543338,0.0003303529,0.0002309977,0.0002953238,0.000005401564,0.0008886976,0.004642187],"genre_scores_gemma":[0.2584295,0.00005239849,0.7410274,0.0003237083,0.00002335382,0.000071304,0.000005434806,0.00001478223,0.0000521564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4604225,"threshold_uncertainty_score":0.8438769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01471843890333308,"score_gpt":0.2519128849429592,"score_spread":0.2371944460396261,"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."}}