1 code implementation • 16 Oct 2022 • Yuyuan Zeng, Bowen Zhao, Shanzhao Qiu, Tao Dai, Shu-Tao Xia
Most existing methods mainly focus on extracting global features from tampered images, while neglecting the relationships of local features between tampered and authentic regions within a single tampered image.
1 code implementation • ICLR 2021 • Yang Bai, Yuyuan Zeng, Yong Jiang, Shu-Tao Xia, Xingjun Ma, Yisen Wang
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs).
1 code implementation • ECCV 2020 • Yang Bai, Yuyuan Zeng, Yong Jiang, Yisen Wang, Shu-Tao Xia, Weiwei Guo
Deep neural networks (DNNs) have demonstrated excellent performance on various tasks, however they are under the risk of adversarial examples that can be easily generated when the target model is accessible to an attacker (white-box setting).
1 code implementation • ECCV 2020 • Haoyu Liang, Zhihao Ouyang, Yuyuan Zeng, Hang Su, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang
Most existing works attempt post-hoc interpretation on a pre-trained model, while neglecting to reduce the entanglement underlying the model.
no code implementations • 25 Sep 2019 • Haoyu Liang, Zhihao Ouyang, Hang Su, Yuyuan Zeng, Zihao He, Shu-Tao Xia, Jun Zhu, Bo Zhang
Convolutional neural networks (CNNs) have often been treated as “black-box” and successfully used in a range of tasks.