Succinct zero-knowledge proofs can certify that a fine-tuned model differs from a base model only by norm-bounded, low-rank, or sparse parameter drift, with cost set by that structure rather than model size.
Defending against weight-poisoning backdoor attacks for parameter-efficient fine-tuning
2 Pith papers cite this work. Polarity classification is still indexing.
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Catastrophic overfitting in fast adversarial training is reinterpreted as a weak-trigger variant of unlearnable tasks, allowing backdoor-inspired recalibration and outlier suppression to restore robustness.
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Fine-Tuning Integrity for Modern Neural Networks: Structured Drift Proofs via Norm, Rank, and Sparsity Certificates
Succinct zero-knowledge proofs can certify that a fine-tuned model differs from a base model only by norm-bounded, low-rank, or sparse parameter drift, with cost set by that structure rather than model size.
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Unveiling the Backdoor Mechanism Hidden Behind Catastrophic Overfitting in Fast Adversarial Training
Catastrophic overfitting in fast adversarial training is reinterpreted as a weak-trigger variant of unlearnable tasks, allowing backdoor-inspired recalibration and outlier suppression to restore robustness.