From the 1 of 5 linked papers with an AI index.
5 papers
When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training
Na Li, Boyu Kuang, Hongsheng Hu +4
The paper shows that mixing text‑to‑image synthetic data with real data during training can increase privacy leakage of the real samples, and introduces a method (RSMixLeak) to mea…
From Multiplicity to Vulnerability: Privacy Amplification Risk from One-Dataset-Multiple-Model Exposure
Qirui Huang, Na Li, Hongsheng Hu +3
To efficiently exploit a valuable data source (e.g., facial or medical images), it is frequently harnessed to fulfill multiple learning objectives (e.g., facial recognition, age es…
CausShield: Sample Reconstruction-Resilient Vertical FL via Causal Representation Learning
Yongqi Jiang, Yansong Gao, Siguang Chen +1
Vertical federated learning (VFL) is a distributed learning paradigm that leverages vertically partitioned features across isolated parties without sharing raw samples; however, it…
Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning
Zhiyang Dai, Yansong Gao, Boyu Kuang +5
Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible, reliance on third-party or internet d…
ArmSSL: Adversarial Robust Black-Box Watermarking for Self-Supervised Learning Pre-trained Encoders
Yongqi Jiang, Yansong Gao, Boyu Kuang +3
Self-supervised learning (SSL) encoders are invaluable intellectual property (IP). However, no existing SSL watermarking for IP protection can concurrently satisfy the following tw…