Cover Image

Model-Guardian: Protecting against Data-Free Model Stealing Using Gradient Representations and Deceptive Predictions

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Yang, Yunfei, Chen, Xiaojun, Xuan, Yuexin, Zhao, Zhendong
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

DeepTracer: Tracing Stolen Model via Deep Coupled Watermarks
by: Yang, Yunfei, et al.
Published: (2025)

Model Stealing Attack against Graph Classification with Authenticity, Uncertainty and Diversity
by: Zhu, Zhihao, et al.
Published: (2023)

GRID: Protecting Training Graph from Link Stealing Attacks on GNN Models
by: Lou, Jiadong, et al.
Published: (2025)

Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
by: Feng, Shanglun, et al.
Published: (2024)

On Stealing Graph Neural Network Models
by: Podhajski, Marcin, et al.
Published: (2025)

Prompt Stealing Attacks Against Text-to-Image Generation Models
by: Shen, Xinyue, et al.
Published: (2023)

Stealix: Model Stealing via Prompt Evolution
by: Zhuang, Zhixiong, et al.
Published: (2025)

DM4Steal: Diffusion Model For Link Stealing Attack On Graph Neural Networks
by: Chen, Jinyin, et al.
Published: (2024)

Watermark Stealing in Large Language Models
by: Jovanović, Nikola, et al.
Published: (2024)

Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation
by: Küchler, Nicolas, et al.
Published: (2025)

Exploring Query Efficient Data Generation towards Data-free Model Stealing in Hard Label Setting
by: Pei, Gaozheng, et al.
Published: (2024)

Refiner: Data Refining against Gradient Leakage Attacks in Federated Learning
by: Fan, Mingyuan, et al.
Published: (2022)

How Feasible is Augmenting Fake Nodes with Learnable Features as a Counter-strategy against Link Stealing Attacks?
by: Mostafiz, Mir Imtiaz, et al.
Published: (2025)

I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks
by: Oliynyk, Daryna, et al.
Published: (2025)

FDINet: Protecting against DNN Model Extraction via Feature Distortion Index
by: Yao, Hongwei, et al.
Published: (2023)

Link Stealing Attacks Against Inductive Graph Neural Networks
by: Wu, Yixin, et al.
Published: (2024)

Transpose Attack: Stealing Datasets with Bidirectional Training
by: Amit, Guy, et al.
Published: (2023)

Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels
by: Shukla, Shubhi, et al.
Published: (2024)

Revisiting Gradient Pruning: A Dual Realization for Defending against Gradient Attacks
by: Xue, Lulu, et al.
Published: (2024)

Efficient Data-Free Model Stealing with Label Diversity
by: Liu, Yiyong, et al.
Published: (2024)

Extracting Spatiotemporal Data from Gradients with Large Language Models
by: Zheng, Lele, et al.
Published: (2024)

Stealing Training Graphs from Graph Neural Networks
by: Lin, Minhua, et al.
Published: (2024)

LinkThief: Combining Generalized Structure Knowledge with Node Similarity for Link Stealing Attack against GNN
by: Zhang, Yuxing, et al.
Published: (2024)

Stealthy Imitation: Reward-guided Environment-free Policy Stealing
by: Zhuang, Zhixiong, et al.
Published: (2024)

ComMark: Covert and Robust Black-Box Model Watermarking with Compressed Samples
by: Yang, Yunfei, et al.
Published: (2025)

Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks
by: Kundu, Satwik, et al.
Published: (2024)

TrEEStealer: Stealing Decision Trees via Enclave Side Channels
by: Sander, Jonas, et al.
Published: (2026)

Strong Copyright Protection for Language Models via Adaptive Model Fusion
by: Abad, Javier, et al.
Published: (2024)

Gradient-Free Privacy Leakage in Federated Language Models through Selective Weight Tampering
by: Rashid, Md Rafi Ur, et al.
Published: (2023)

Examining the Threat Landscape: Foundation Models and Model Stealing
by: Raj, Ankita, et al.
Published: (2025)

Does Training with Synthetic Data Truly Protect Privacy?
by: Zhao, Yunpeng, et al.
Published: (2025)

Protecting Copyright of Medical Pre-trained Language Models: Training-Free Backdoor Model Watermarking
by: Kong, Cong, et al.
Published: (2024)

Consistent Valid Physically-Realizable Adversarial Attack against Crowd-flow Prediction Models
by: Ali, Hassan, et al.
Published: (2023)

Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
by: Huang, Catherine, et al.
Published: (2024)

Intrinsic Fingerprint of LLMs: Continue Training is NOT All You Need to Steal A Model!
by: Yoon, Do-hyeon, et al.
Published: (2025)

Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion
by: Liu, Xuan, et al.
Published: (2024)

Dark Deceptions in DHCP: Dismantling Network Defenses
by: Dilworth, Robert
Published: (2025)

Teach LLMs to Phish: Stealing Private Information from Language Models
by: Panda, Ashwinee, et al.
Published: (2024)

TUNI: A Textual Unimodal Detector for Identity Inference in CLIP Models
by: Li, Songze, et al.
Published: (2024)

QuantumLeak: Stealing Quantum Neural Networks from Cloud-based NISQ Machines
by: Fu, Zhenxiao, et al.
Published: (2024)