1 code implementation • 13 Dec 2023 • Pei Yan, Shunquan Tan, Miaohui Wang, Jiwu Huang
As a significant representation of dynamic malware behavior, the API (Application Programming Interface) sequence, comprised of consecutive API calls, has progressively become the dominant feature of dynamic analysis methods.
no code implementations • 16 Oct 2023 • Long Zhuo, Shenghai Luo, Shunquan Tan, Han Chen, Bin Li, Jiwu Huang
In adversarial training, SEAR employs a forgery localization model as a supervisor to explore tampering features and constructs a deep-learning concealer to erase corresponding traces.
no code implementations • 20 Sep 2023 • Anwei Luo, Rizhao Cai, Chenqi Kong, Xiangui Kang, Jiwu Huang, Alex C. Kot
To circumvent these issues, we propose a novel Forgery-aware Adaptive Vision Transformer (FA-ViT).
no code implementations • 24 Apr 2023 • Anwei Luo, Chenqi Kong, Jiwu Huang, Yongjian Hu, Xiangui Kang, Alex C. Kot
Face forgery detection is essential in combating malicious digital face attacks.
1 code implementation • 8 Nov 2022 • Peiyu Zhuang, Haodong Li, Rui Yang, Jiwu Huang
The ReLoc framework mainly consists of an image restoration module and a tampering localization module.
no code implementations • 5 Sep 2022 • Changsheng chen, Lin Zhao, Rizhao Cai, Zitong Yu, Jiwu Huang, Alex C. Kot
We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing and recaptured document detection tasks.
1 code implementation • 12 Jun 2022 • Shunquan Tan, Qiushi Li, Laiyuan Li, Bin Li, Jiwu Huang
We propose a normalized distortion threshold to evaluate the sensitivity of each involved convolutional layer of the base model to guide STD-NET to compress target network in an efficient and unsupervised approach, and obtain two network structures of different shapes with low computation cost and similar performance compared with the original one.
no code implementations • 24 Nov 2021 • Kangkang Wei, Weiqi Luo, Shunquan Tan, Jiwu Huang
The proposed method includes preprocessing, convolutional, and classification modules.
no code implementations • 30 Aug 2021 • Xianhao Tian, Peijia Zheng, Jiwu Huang
In this paper, we propose an efficient and robust privacy-preserving motion detection and multiple object tracking scheme for encrypted surveillance video bitstreams.
1 code implementation • 6 Jul 2021 • Long Zhuo, Shunquan Tan, Bin Li, Jiwu Huang
In this paper, we propose a self-adversarial training strategy and a reliable coarse-to-fine network that utilizes a self-attention mechanism to localize forged regions in forgery images.
no code implementations • 24 Jun 2021 • Wei Lu, Lingyi Liu, Junwei Luo, Xianfeng Zhao, Yicong Zhou, Jiwu Huang
And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively.
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.
1 code implementation • 25 Mar 2021 • Xianbo Mo, Shunquan Tan, Bin Li, Jiwu Huang
Recent research has shown that non-additive image steganographic frameworks effectively improve security performance through adjusting distortion distribution.
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.
1 code implementation • 22 Aug 2018 • Haodong Li, Bin Li, Shunquan Tan, Jiwu Huang
In this paper, we address the problem of detecting deep network generated (DNG) images by analyzing the disparities in color components between real scene images and DNG images.
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