Guang Yang

The paper proposes AnaFP, a theoretically guided analytical fingerprinting scheme that determines the optimal distance of a model's fingerprint from the decision boundary to ensure both robustness and uniqueness for model ownership protection.

IrisFP introduces a novel adversarial-example-based framework that generates composite-sample fingerprints near the intersection of multiple decision boundaries, significantly enhancing model ownership verification robustness and uniqueness.

LiteGuard proposes an efficient task-agnostic model fingerprinting framework that achieves enhanced generalization and significantly reduces computational overhead compared to existing methods like MetaV.

The paper proposes Asymmetric Phase Coding (APC), a training-free cryptographic audio watermarking scheme that achieves high extraction rates (97.5%-98.3%) across various real-world and adversarial attacks.

The paper proposes interaction-layer antidistillation watermarks by embedding behavioral markers into the system prompt, which successfully track knowledge distillation even when paraphrasing attackers strip traditional token-level signals.