ask for academic clarification(Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction)
Recently, when I was studying the code you released about “bob.paper.iccv2023_face_ti”, I came across a problem.Here are the questions:
For your evaluation code evaluation_pipeline.py, when evaluated with your trained model, the results are quite different from the data given in the literature. For example, using the trained model ElasticFace-ArcFace_loss.pth, I set FR_system to ElasticFace and FR_target to ArcFace in the evaluation_pipeline.py file to perform black-box attacks on the LFW dataset. We will get scores_inversion-dev.csv and scores-dev.csv, and then compute the SAR by eval_SAR_TMR.py
FMR: 0.01 threshold: -0.8191349171709174 TMR: 0.976, SAR: 0.5722518676627535
FMR: 0.001 threshold: -0.7618033404275485 TMR: 0.964, SAR: 0.3755602988260406
ask for academic clarification(Template Inversion Attack against Face Recognition Systems using 3D Face Reconstruction)
Recently, when I was studying the code you released about “bob.paper.iccv2023_face_ti”, I came across a problem.Here are the questions:
For your evaluation code evaluation_pipeline.py, when evaluated with your trained model, the results are quite different from the data given in the literature. For example, using the trained model ElasticFace-ArcFace_loss.pth, I set FR_system to ElasticFace and FR_target to ArcFace in the evaluation_pipeline.py file to perform black-box attacks on the LFW dataset. We will get scores_inversion-dev.csv and scores-dev.csv, and then compute the SAR by eval_SAR_TMR.py
FMR: 0.01 threshold: -0.8191349171709174 TMR: 0.976, SAR: 0.5722518676627535
FMR: 0.001 threshold: -0.7618033404275485 TMR: 0.964, SAR: 0.3755602988260406