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
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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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.
The paper formalizes fixed-set worst-case corruption in PBE, implements corruption searches on a string DSL, and shows VPA recovers some margin-1 tasks but fails on public SyGuS where vote margins are near one.
An explainability-aware L0 penalty yields coherent counterfactual edits, and the same geometry defines a Tolerance-Region Confusion Matrix that quantifies class-to-class fragility under interpretable perturbations.
citing papers explorer
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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.
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Fixed-Set Robustness in Programming by Example: Example Corruption and Semantic Partition Recovery
The paper formalizes fixed-set worst-case corruption in PBE, implements corruption searches on a string DSL, and shows VPA recovers some margin-1 tasks but fails on public SyGuS where vote margins are near one.
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Optimized Instance Alteration for Explaining and Assessing Robustness of Classifiers
An explainability-aware L0 penalty yields coherent counterfactual edits, and the same geometry defines a Tolerance-Region Confusion Matrix that quantifies class-to-class fragility under interpretable perturbations.