{"total":21,"rows":[{"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":"2305.10862","title":"How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses","created":"2023-05-18","cited_by_count":66,"area":null},{"id":"2309.10058","title":"Dual Student Networks for Data-Free Model Stealing","created":"2023-09-18","cited_by_count":17,"area":null},{"id":"2411.10023","title":"Model Inversion Attacks: A Survey of Approaches and Countermeasures","created":"2024-11-15","cited_by_count":11,"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":"2403.14772","title":"Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures","created":"2024-03-21","cited_by_count":8,"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":"2312.05924","title":"Data-Free Hard-Label Robustness Stealing Attack","created":"2023-12-10","cited_by_count":6,"area":null},{"id":"2203.10923","title":"TinyMLOps: Operational Challenges for Widespread Edge AI Adoption","created":"2022-03-21","cited_by_count":3,"area":null},{"id":"2309.01786","title":"Safe and Robust Watermark Injection with a Single OoD Image","created":"2023-09-04","cited_by_count":3,"area":null},{"id":"2209.06300","title":"PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models","created":"2022-09-13","cited_by_count":2,"area":null},{"id":"2412.20087","title":"On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs","created":"2024-12-28","cited_by_count":2,"area":null},{"id":"2305.13584","title":"A Model Stealing Attack Against Multi-Exit Networks","created":"2023-05-23","cited_by_count":1,"area":null},{"id":"2310.09792","title":"SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack","created":"2023-10-15","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":"2502.19502","title":"Models That Are Interpretable But Not Transparent","created":"2025-02-26","cited_by_count":1,"area":null},{"id":"2312.14677","title":"MEAOD: Model Extraction Attack against Object Detectors","created":"2023-12-22","cited_by_count":0,"area":null},{"id":"2402.11953","title":"Stealing the Invisible: Unveiling Pre-Trained CNN Models through Adversarial Examples and Timing Side-Channels","created":"2024-02-19","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":"2508.21654","title":"I Stolenly Swear That I Am Up to (No) Good: Design and Evaluation of Model Stealing Attacks","created":"2025-08-29","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}],"next_cursor":null}