no code implementations • 13 Oct 2023 • Junlei Zhou, Jiashi Gao, Ziwei Wang, Xuetao Wei
Previous work only focused on data attribution from the training data perspective, which is unsuitable for tracing and quantifying copyright infringement in practice because of the following reasons: (1) the training datasets are not always available in public; (2) the model provider is the responsible party, not the image.
no code implementations • 28 Sep 2023 • Jiashi Gao, Changwu Huang, Ming Tang, Shin Hwei Tan, Xin Yao, Xuetao Wei
Recent advances in federated learning (FL) enable collaborative training of machine learning (ML) models from large-scale and widely dispersed clients while protecting their privacy.
no code implementations • 25 Oct 2022 • Guanhui Ye, Jiashi Gao, Wei Xie, Bo Yin, Xuetao Wei
In this paper, we propose DBMARK, a novel end-to-end digital image watermarking framework to deep boost the robustness of DNN-based image watermarking.
no code implementations • 8 Feb 2022 • Jiashi Gao, Xinming Shi, James J. Q. Yu
Pedestrian trajectory forecasting is a fundamental task in multiple utility areas, such as self-driving, autonomous robots, and surveillance systems.