{"total":20,"rows":[{"id":"2308.00958","title":"Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks","created":"2023-08-02","cited_by_count":10,"area":null},{"id":"2210.13631","title":"On the Robustness of Dataset Inference","created":"2022-10-24","cited_by_count":8,"area":null},{"id":"2506.02362","title":"MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models","created":"2025-06-03","cited_by_count":8,"area":null},{"id":"2406.18540","title":"Fully Exploiting Every Real Sample: SuperPixel Sample Gradient Model Stealing","created":"2024-05-18","cited_by_count":5,"area":null},{"id":"2502.02438","title":"Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment","created":"2025-02-04","cited_by_count":5,"area":null},{"id":"2502.20924","title":"Decoder Gradient Shield: Provable and High-Fidelity Prevention of Gradient-Based Box-Free Watermark Removal","created":"2025-02-28","cited_by_count":5,"area":null},{"id":"2306.11338","title":"FDINet: Protecting against DNN Model Extraction via Feature Distortion Index","created":"2023-06-20","cited_by_count":3,"area":null},{"id":"2502.18077","title":"Examining the Threat Landscape: Foundation Models and Model Stealing","created":"2025-02-25","cited_by_count":2,"area":null},{"id":"2309.01838","title":"Efficient Defense Against Model Stealing Attacks on Convolutional Neural Networks","created":"2023-09-04","cited_by_count":1,"area":null},{"id":"2503.12497","title":"D-ADD: An Effective Plug-In for Defending Against Model Stealing","created":"2025-03-16","cited_by_count":1,"area":null},{"id":"2601.11952","title":"Decoder Gradient Shields: A Family of Provable and High-Fidelity Methods Against Gradient-Based Box-Free Watermark Removal","created":"2026-01-17","cited_by_count":1,"area":null},{"id":"2401.05895","title":"Binary Linear Tree Commitment-based Ownership Protection for Distributed Machine Learning","created":"2024-01-11","cited_by_count":0,"area":null},{"id":"2411.07468","title":"Privacy-Preserving Verifiable Neural Network Inference Service","created":"2024-11-12","cited_by_count":0,"area":null},{"id":"2503.06188","title":"Attackers Can Do Better: Over- and Understated Factors of Model Stealing Attacks","created":"2025-03-08","cited_by_count":0,"area":null},{"id":"2506.05867","title":"Stealix: Model Stealing via Prompt Evolution","created":"2025-06-06","cited_by_count":0,"area":null},{"id":"2506.19464","title":"Assessing Risk of Stealing Proprietary Models for Medical Imaging Tasks","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2509.16546","title":"Train to Defend: First Defense Against Cryptanalytic Neural Network Parameter Extraction Attacks","created":"2025-09-20","cited_by_count":0,"area":null},{"id":"2512.23948","title":"DivQAT: Enhancing Robustness of Quantized Convolutional Neural Networks against Model Extraction Attacks","created":"2025-12-30","cited_by_count":0,"area":null},{"id":"2608.01023","title":"Caliber: Cross-Architecture Extraction-Cost Control for Score-Returning APIs","created":"2026-08-02","cited_by_count":0,"area":null},{"id":"2608.11495","title":"Defending against Model Extraction for GNNs with Model Reprogramming","created":"2026-08-11","cited_by_count":0,"area":null}],"next_cursor":null}