D-ADD: An Effective Plug-In for Defending Against Model Stealing

arXiv:2503.12497 · cs.CR, cs.AI · Submitted 2025-03-16 · Read on arXiv

cs.CR, cs.AI

Submitted: 2025-03-16

Updated: 2026-09-06

Comments: 11 pages, 7 figures, published in AAAI 2025

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Accuracy — 17× in this paper · explained in 1 episode
Malicious Score — 9× in this paper
Robustness — 8× in this paper · explained in 5 episodes
Distribution Shift — 6× in this paper · explained in 6 episodes
AUROC — 3× in this paper · explained in 2 episodes
Adversarial Attacks — 3× in this paper · explained in 1 episode
Distillation — 3× in this paper · explained in 2 episodes
Knowledge Distillation — 3× in this paper · explained in 11 episodes
Account-aware Distribution Discrepancy — 2× in this paper
Adversarial Examples — 2× in this paper · explained in 2 episodes
Contrastive Learning — 2× in this paper · explained in 5 episodes
Decoupling — 1× in this paper · explained in 2 episodes
Domain Shift — 1× in this paper · explained in 4 episodes
Equal Weights — 1× in this paper
Global Distribution Distance — 1× in this paper
Multi-Variate Normal — 1× in this paper
Out-of-Distribution (OOD) Detection — 1× in this paper · explained in 2 episodes
Quantization — 1× in this paper · explained in 12 episodes
Representation Learning — 1× in this paper · explained in 2 episodes
Semantic Gap — 1× in this paper · explained in 2 episodes