{"total":21,"rows":[{"id":"2212.14315","title":"\"Real Attackers Don't Compute Gradients\": Bridging the Gap Between Adversarial ML Research and Practice","created":"2022-12-29","cited_by_count":56,"area":null},{"id":"2112.08331","title":"Model Stealing Attacks Against Inductive Graph Neural Networks","created":"2021-12-15","cited_by_count":50,"area":null},{"id":"2201.11692","title":"SSLGuard: A Watermarking Scheme for Self-supervised Learning Pre-trained Encoders","created":"2022-01-27","cited_by_count":45,"area":null},{"id":"2209.09024","title":"Dataset Inference for Self-Supervised Models","created":"2022-09-16","cited_by_count":36,"area":null},{"id":"2404.17399","title":"Evaluations of Machine Learning Privacy Defenses are Misleading","created":"2024-04-26","cited_by_count":36,"area":null},{"id":"2307.12280","title":"Downstream-agnostic Adversarial Examples","created":"2023-07-23","cited_by_count":32,"area":null},{"id":"2201.07513","title":"Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders","created":"2022-01-19","cited_by_count":31,"area":null},{"id":"2208.11180","title":"Auditing Membership Leakages of Multi-Exit Networks","created":"2022-08-23","cited_by_count":27,"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":"2404.17202","title":"Self-supervised visual learning in the low-data regime: a comparative evaluation","created":"2024-04-26","cited_by_count":11,"area":null},{"id":"2303.09767","title":"It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness","created":"2023-03-17","cited_by_count":9,"area":null},{"id":"2210.11049","title":"How Does a Deep Learning Model Architecture Impact Its Privacy? A Comprehensive Study of Privacy Attacks on CNNs and Transformers","created":"2022-10-20","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":"2306.08173","title":"Safeguarding Data in Multimodal AI: A Differentially Private Approach to CLIP Training","created":"2023-06-13","cited_by_count":7,"area":null},{"id":"2403.02571","title":"DPAdapter: Improving Differentially Private Deep Learning through Noise Tolerance Pre-training","created":"2024-03-05","cited_by_count":7,"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":"2409.19413","title":"Membership Privacy Evaluation in Deep Spiking Neural Networks","created":"2024-09-28","cited_by_count":3,"area":null},{"id":"2310.10124","title":"A Comprehensive Study of Privacy Risks in Curriculum Learning","created":"2023-10-16","cited_by_count":2,"area":null},{"id":"2504.05651","title":"Measuring D\\'ej\\`a vu Memorization Efficiently","created":"2025-04-08","cited_by_count":2,"area":null},{"id":"2205.12430","title":"Additive Logistic Mechanism for Privacy-Preserving Self-Supervised Learning","created":"2022-05-25","cited_by_count":0,"area":null},{"id":"2310.03104","title":"Differentially Private Optimization for Non-Decomposable Objective Functions","created":"2023-10-04","cited_by_count":0,"area":null}],"next_cursor":null}