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.
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.
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.
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 • 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.
Multimedia
no code implementations • 16 Oct 2017 • Bin Li, Hu Luo, Haoxin Zhang, Shunquan Tan, Zhongzhou Ji
In this paper, we present a CNN solution by using raw DCT (discrete cosine transformation) coefficients from JPEG images as input.