{"id":"W4416214887","doi":"10.1109/tmm.2025.3632696","title":"Fast and Effective Overwrite Attack Against DNN-Based Image Watermarking Models","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Adversarial Robustness in Machine Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Digital watermarking; Robustness (evolution); Watermark; Noise (video); Image (mathematics); Watermarking attack; Vulnerability (computing); Artificial neural network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007749634,0.0008748916,0.0007950323,0.0009208973,0.001264906,0.0006359278,0.0009700651,0.0004670625,0.00006046923],"category_scores_gemma":[0.00006761013,0.0009402554,0.0003802548,0.001152942,0.0006273601,0.001201436,0.00003115123,0.001979253,0.0001003425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004917536,"about_ca_system_score_gemma":0.0003602927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009770968,"about_ca_topic_score_gemma":0.00003493532,"domain_scores_codex":[0.994996,0.0008095449,0.0007973647,0.001680689,0.0006913031,0.001025103],"domain_scores_gemma":[0.9958572,0.002105004,0.0002664356,0.001134443,0.0002806636,0.0003562883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001751487,0.0002440907,0.00001696266,0.0001543478,0.0001452617,0.00004780779,0.001549904,0.635195,0.003236631,0.00001986885,0.00003061679,0.3591844],"study_design_scores_gemma":[0.003758341,0.0001711234,0.0002650587,0.0008503264,0.0001997686,0.000004931036,0.00009815883,0.9722528,0.02138389,0.00008625471,0.0001631419,0.0007661493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02057456,0.0001487045,0.9704215,0.0009156966,0.004453898,0.00139285,0.00007251321,0.0002680355,0.001752226],"genre_scores_gemma":[0.9150996,0.0001117664,0.08290602,0.0006939687,0.000136902,0.0001547205,0.000005988689,0.00006646608,0.0008245249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8945251,"threshold_uncertainty_score":0.9993048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01282977566655843,"score_gpt":0.2746773812058975,"score_spread":0.2618476055393391,"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."}}