Standard model inversion evaluation counts many adversarial false positives as successes; MLLM-based evaluation reveals consistently high false-positive rates across 27 attack setups.
Model inversion robustness: Can transfer learning help? InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12183–12193, 2024
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Revisiting Model Inversion Evaluation: From Misleading Standards to Reliable Privacy Assessment
Standard model inversion evaluation counts many adversarial false positives as successes; MLLM-based evaluation reveals consistently high false-positive rates across 27 attack setups.