MetaCloak-JPEG uses a DiffJPEG layer with straight-through estimator inside a JPEG-aware EOT and curriculum meta-learning loop to produce l-inf bounded perturbations that retain 91.3% effectiveness after real JPEG compression.
Towards deep learning models resistant to adversarial attacks
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The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.
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MetaCloak-JPEG: JPEG-Robust Adversarial Perturbation for Preventing Unauthorized DreamBooth-Based Deepfake Generation
MetaCloak-JPEG uses a DiffJPEG layer with straight-through estimator inside a JPEG-aware EOT and curriculum meta-learning loop to produce l-inf bounded perturbations that retain 91.3% effectiveness after real JPEG compression.
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SoK: Critical Evaluation of Quantum Machine Learning for Adversarial Robustness
The paper delivers the first comprehensive systematization of adversarial robustness in QML with new empirical tests showing an accuracy-robustness trade-off, amplitude encoding's vulnerability, and QML's greater susceptibility to evasion attacks than classical models.