no code implementations • 27 Feb 2024 • Yu Ran, Ao-Xiang Zhang, Mingjie Li, Weixuan Tang, Yuan-Gen Wang
Specifically, we first formulate the attack problem as maximizing the deviation between the estimated quality scores of original and perturbed images, while restricting the perturbed image distortions for visual quality preservation.
1 code implementation • NeurIPS 2023 • Ao-Xiang Zhang, Yu Ran, Weixuan Tang, Yuan-Gen Wang
In this paper, we make the first attempt to evaluate the robustness of NR-VQA models against adversarial attacks, and propose a patch-based random search method for black-box attack.
1 code implementation • journal 2023 • Weixiang Li, Shiang Wu, Bin Li, Weixuan Tang, and XinPeng Zhang
This framework directly learns universal costs that can be applied to any payload.
1 code implementation • 21 Jun 2023 • Fengchuang Xing, Yuan-Gen Wang, Weixuan Tang, Guopu Zhu, Sam Kwong
Self-attention based Transformer has achieved great success in many computer vision tasks.
no code implementations • 9 Oct 2022 • Ao-Xiang Zhang, Yuan-Gen Wang, Weixuan Tang, Leida Li, Sam Kwong
Based on the revisited HVS, a no-reference VQA framework called HVS-5M (NRVQA framework with five modules simulating HVS with five characteristics) is proposed.
Ranked #6 on Video Quality Assessment on LIVE-FB LSVQ
1 code implementation • CVPR 2022 • Weixuan Tang, Danping Zou
We address the problem of estimating the poses of multiple instances of the source point cloud within a target point cloud.
no code implementations • 9 May 2021 • Weixuan Tang, Bin Li, Mauro Barni, Jin Li, Jiwu Huang
To address the issue, in this paper we extend an existing automatic cost learning scheme to JPEG, where the proposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning) is explicitly designed to tailor the JPEG DCT structure.
no code implementations • 13 Jan 2021 • Xinghong Qin, Shunquan Tan, Bin Li, Weixuan Tang, Jiwu Huang
In this paper, we present a novel steganography scheme denoted as ITE-SYN (based on ITEratively adversarial perturbations onto a SYNchronized-directions sub-image), by which security data is embedded with synchronizing modification directions to enhance security and then iteratively increased perturbations are added onto a sub-image to reduce loss with cover class label of the target CNN classifier.
1 code implementation • journal 2020 • Weixuan Tang, Bin Li, Mauro Barni, Jin Li, Jiwu Huang
In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment.