{"total":46,"rows":[{"id":"2108.00401","title":"Advances in adversarial attacks and defenses in computer vision: A survey","created":"2021-08-01","cited_by_count":127,"area":null},{"id":"2206.08451","title":"I Know What You Trained Last Summer: A Survey on Stealing Machine Learning Models and Defences","created":"2022-06-16","cited_by_count":75,"area":null},{"id":"2204.11022","title":"Towards Data-Free Model Stealing in a Hard Label Setting","created":"2022-04-23","cited_by_count":58,"area":null},{"id":"2112.03476","title":"Defending against Model Stealing via Verifying Embedded External Features","created":"2021-12-07","cited_by_count":46,"area":null},{"id":"2208.11180","title":"Auditing Membership Leakages of Multi-Exit Networks","created":"2022-08-23","cited_by_count":27,"area":null},{"id":"2105.00623","title":"Black-Box Dissector: Towards Erasing-based Hard-Label Model Stealing Attack","created":"2021-05-03","cited_by_count":25,"area":null},{"id":"2201.05889","title":"StolenEncoder: Stealing Pre-trained Encoders in Self-supervised Learning","created":"2022-01-15","cited_by_count":23,"area":null},{"id":"2201.09243","title":"Increasing the Cost of Model Extraction with Calibrated Proof of Work","created":"2022-01-23","cited_by_count":21,"area":null},{"id":"2206.14157","title":"How to Steer Your Adversary: Targeted and Efficient Model Stealing Defenses with Gradient Redirection","created":"2022-06-28","cited_by_count":21,"area":null},{"id":"2208.02820","title":"MOVE: Effective and Harmless Ownership Verification via Embedded External Features","created":"2022-08-04","cited_by_count":21,"area":null},{"id":"2402.01096","title":"Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance","created":"2024-02-02","cited_by_count":14,"area":null},{"id":"2109.10870","title":"SoK: Machine Learning Governance","created":"2021-09-20","cited_by_count":10,"area":null},{"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":"2302.02162","title":"AUTOLYCUS: Exploiting Explainable AI (XAI) for Model Extraction Attacks against Interpretable Models","created":"2023-02-04","cited_by_count":9,"area":null},{"id":"2404.14016","title":"Ungeneralizable Examples","created":"2024-04-22","cited_by_count":9,"area":null},{"id":"2302.10739","title":"MalProtect: Stateful Defense Against Adversarial Query Attacks in ML-based Malware Detection","created":"2023-02-21","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":"2110.15444","title":"10 Security and Privacy Problems in Large Foundation Models","created":"2021-10-28","cited_by_count":6,"area":null},{"id":"2207.10561","title":"Careful What You Wish For: on the Extraction of Adversarially Trained Models","created":"2022-07-21","cited_by_count":6,"area":null},{"id":"2402.11687","title":"Evaluating Efficacy of Model Stealing Attacks and Defenses on Quantum Neural Networks","created":"2024-02-18","cited_by_count":6,"area":null},{"id":"2502.16065","title":"A Survey of Model Extraction Attacks and Defenses in Distributed Computing Environments","created":"2025-02-22","cited_by_count":6,"area":null},{"id":"2302.11982","title":"A Plot is Worth a Thousand Words: Model Information Stealing Attacks via Scientific Plots","created":"2023-02-23","cited_by_count":5,"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":"2602.20419","title":"CREDIT: Certified Ownership Verification of Deep Neural Networks Against Model Extraction Attacks","created":"2026-02-23","cited_by_count":5,"area":null},{"id":"2310.01959","title":"Beyond Labeling Oracles: What does it mean to steal ML models?","created":"2023-10-03","cited_by_count":4,"area":null},{"id":"2312.10578","title":"SAME: Sample Reconstruction against Model Extraction Attacks","created":"2023-12-17","cited_by_count":4,"area":null},{"id":"2104.12623","title":"Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models","created":"2021-04-26","cited_by_count":3,"area":null},{"id":"2106.11424","title":"HODA: Hardness-Oriented Detection of Model Extraction Attacks","created":"2021-06-21","cited_by_count":3,"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":"2606.15810","title":"Let Them Steal: Trapping Large Language Model Extraction Attacks with Knowledge Honeypot","created":"2026-06-14","cited_by_count":3,"area":null},{"id":"2307.01292","title":"Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems","created":"2023-07-03","cited_by_count":2,"area":null},{"id":"2503.00065","title":"ADAGE: Active Defenses Against GNN Extraction","created":"2025-02-27","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":"2310.16335","title":"Defense Against Model Extraction Attacks on Recommender Systems","created":"2023-10-25","cited_by_count":1,"area":null},{"id":"2312.15036","title":"SODA: Protecting Proprietary Information in On-Device Machine Learning Models","created":"2023-12-22","cited_by_count":1,"area":null},{"id":"2407.03883","title":"Protecting Deep Learning Model Copyrights with Adversarial Example-Free Reuse Detection","created":"2024-07-04","cited_by_count":1,"area":null},{"id":"2502.15567","title":"Model Privacy: A Unified Framework for Understanding Model Stealing Attacks and Defenses","created":"2025-02-21","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":"2503.18081","title":"Model-Guardian: Protecting against Data-Free Model Stealing Using Gradient Representations and Deceptive Predictions","created":"2025-03-23","cited_by_count":1,"area":null},{"id":"2605.18829","title":"Lossless Anti-Distillation Sampling","created":"2026-05-12","cited_by_count":1,"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":"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":"2602.22238","title":"TT-SEAL: TTD-Aware Selective Encryption for Adversarially-Robust and Low-Latency Edge AI","created":"2026-02-24","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}