{"id":"W3184732184","doi":"10.18280/rces.080205","title":"Various Image Processing Attacks for Image Watermarking in the Wavelet Domain Using Singular Value Decomposition and Discrete Cosine Transform","year":2021,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Advanced Steganography and Watermarking Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Digital watermarking; Discrete cosine transform; Singular value decomposition; Watermark; Computer science; Discrete wavelet transform; Sharpening; Artificial intelligence; Singular value; Robustness (evolution); Peak signal-to-noise ratio; Computer vision; Gaussian noise; Modified discrete cosine transform; Mathematics; Wavelet transform; Algorithm; Wavelet; Transform coding; Image (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":[0.0004603,0.0004452429,0.0005107324,0.001056052,0.0002209836,0.0004321828,0.0002626782,0.0005037673,0.0005912039],"category_scores_gemma":[0.001148521,0.0001460038,0.00069571,0.00114924,0.0003802644,0.000624968,0.0002276729,0.0005503272,0.0003074079],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002381427,"about_ca_system_score_gemma":0.0002838928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003870268,"about_ca_topic_score_gemma":0.0005295274,"domain_scores_codex":[0.9994769,0.00006569509,0.00003698039,0.00005448237,0.000342758,0.00002332411],"domain_scores_gemma":[0.9997354,0.00008756555,0.00003633032,0.0000420709,0.00009009583,0.000008527047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003241686,0.0001604337,0.00119083,0.0008666258,0.0001519561,0.0004691914,0.0001487627,0.01681518,0.3206927,0.01434179,0.001635213,0.6432033],"study_design_scores_gemma":[0.00008541493,0.001292484,0.007114111,0.0002654317,0.0002819904,0.003508888,0.0001847255,0.4055148,0.5273023,0.007753524,0.0465752,0.0001212059],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.144001,0.01865544,0.8264692,0.0006226863,0.0004954172,0.0002019994,0.0001028795,0.0005906728,0.008860709],"genre_scores_gemma":[0.5115707,0.02615726,0.4507293,0.0002123811,0.0002428874,0.0001634047,0.000385395,0.00008903929,0.01044966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001056052,"threshold_uncertainty_score":0.002434313,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01342180203797035,"score_gpt":0.3094432029763598,"score_spread":0.2960214009383895,"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."}}