{"total":23,"rows":[{"id":"2410.16618","title":"SoK: Dataset Copyright Auditing in Machine Learning Systems","created":"2024-10-22","cited_by_count":15,"area":null},{"id":"2310.08571","title":"Bucks for Buckets (B4B): Active Defenses Against Stealing Encoders","created":"2023-10-12","cited_by_count":8,"area":null},{"id":"2405.14517","title":"TUNI: A Textual Unimodal Detector for Identity Inference in CLIP Models","created":"2024-05-23","cited_by_count":8,"area":null},{"id":"2507.05622","title":"DATABench: Evaluating Dataset Auditing in Deep Learning from an Adversarial Perspective","created":"2025-07-08","cited_by_count":8,"area":null},{"id":"2405.15161","title":"Are You Copying My Prompt? Protecting the Copyright of Vision Prompt for VPaaS via Watermark","created":"2024-05-24","cited_by_count":4,"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":"2506.06057","title":"Hey, That's My Data! Token-Only Dataset Inference in Large Language Models","created":"2025-06-06","cited_by_count":3,"area":null},{"id":"2601.01966","title":"Refinement Provenance Inference: Detecting LLM-Refined Training Prompts from Model Behavior","created":"2026-01-05","cited_by_count":3,"area":null},{"id":"2503.00065","title":"ADAGE: Active Defenses Against GNN Extraction","created":"2025-02-27","cited_by_count":2,"area":null},{"id":"2507.12022","title":"Dataset Ownership Verification for Pre-trained Masked Models","created":"2025-07-16","cited_by_count":2,"area":null},{"id":"2508.01074","title":"Evading Data Provenance in Deep Neural Networks","created":"2025-08-01","cited_by_count":2,"area":null},{"id":"2306.05233","title":"Ownership Protection of Generative Adversarial Networks","created":"2023-06-08","cited_by_count":1,"area":null},{"id":"2308.04673","title":"SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning","created":"2023-08-09","cited_by_count":1,"area":null},{"id":"2506.16196","title":"Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs","created":"2025-06-19","cited_by_count":1,"area":null},{"id":"2512.14439","title":"VICTOR: Dataset Copyright Auditing in Video Recognition Systems","created":"2025-12-16","cited_by_count":1,"area":null},{"id":"2401.11723","title":"Unraveling Attacks in Machine Learning-based IoT Ecosystems: A Survey and the Open Libraries Behind Them","created":"2024-01-22","cited_by_count":0,"area":null},{"id":"2405.04108","title":"A2-DIDM: Privacy-preserving Accumulator-enabled Auditing for Distributed Identity of DNN Model","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2408.02814","title":"Pre-trained Encoder Inference: Revealing Upstream Encoders In Downstream Machine Learning Services","created":"2024-08-05","cited_by_count":0,"area":null},{"id":"2604.22550","title":"ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders","created":"2026-04-24","cited_by_count":0,"area":null},{"id":"2605.01834","title":"Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning","created":"2026-05-03","cited_by_count":0,"area":null},{"id":"2606.24408","title":"Natural Identifiers for Privacy and Data Audits in Large Language Models","created":"2026-06-23","cited_by_count":0,"area":null},{"id":"2606.26257","title":"Dataset Usage Inference without Shadow Models or Held-out Data","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2609.34232","title":"Trustworthy synthetic visual media: Evidence across the media lifecycle","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}