no code implementations • 5 Feb 2023 • Qiyun Liu, Zhiguang Yang, Hanzhou Wu
However, in order to detect whether secret information is hidden within JEPG images, the majority of existing algorithms are designed in conjunction with the popular computer vision related networks, without considering the key characteristics appeared in image steganalysis.
no code implementations • 30 Sep 2022 • Li Zhang, Yong liu, Shaoteng Liu, Tianshu Yang, Yexin Wang, Xinpeng Zhang, Hanzhou Wu
Intellectual property protection of deep neural networks is receiving attention from more and more researchers, and the latest research applies model watermarking to generative models for image processing.
no code implementations • 9 Sep 2022 • Hanzhou Wu
Deep neural networks (DNNs) have already achieved great success in a lot of application areas and brought profound changes to our society.
no code implementations • 8 Aug 2022 • Tianxing Zhang, Hanzhou Wu, Xiaofeng Lu, Guangling Sun
As a self-supervised learning paradigm, contrastive learning has been widely used to pre-train a powerful encoder as an effective feature extractor for various downstream tasks.
no code implementations • 21 Jun 2022 • Xiaoyan Zheng, Yurun Fang, Hanzhou Wu
To tackle with this problem, in this paper, we propose a general framework to embed secret information into a given cover text, for which the embedded information and the original cover text can be perfectly retrieved from the marked text.
no code implementations • 9 May 2022 • Lina Lin, Hanzhou Wu
Though these methods work very well, they were designed for individual DNN models, which cannot be directly applied to deep ensemble models (DEMs) that combine multiple DNN models to make the final decision.
no code implementations • 26 Mar 2022 • Xiaoyan Zheng, Hanzhou Wu
Linguistic steganography (LS) conceals the presence of communication by embedding secret information into a text.
no code implementations • 8 Mar 2022 • Tianyu Yang, Hanzhou Wu, Biao Yi, Guorui Feng, Xinpeng Zhang
In this paper, we propose a novel LS method to modify a given text by pivoting it between two different languages and embed secret data by applying a GLS-like information encoding strategy.
no code implementations • 25 Oct 2021 • Mingjie Li, Hanzhou Wu, Xinpeng Zhang
Adversarial example generation has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals the vulnerability of DNNs, motivating us to find good solutions to improve the robustness of DNN models.
no code implementations • 3 Oct 2021 • Qiyun Liu, Hanzhou Wu
In this paper, we introduce a graph representation learning architecture for spatial image steganalysis, which is motivated by the assumption that steganographic modifications unavoidably distort the statistical characteristics of the hidden graph features derived from cover images.
no code implementations • 26 Jul 2021 • Biao Yi, Hanzhou Wu, Guorui Feng, Xinpeng Zhang
Such kind of difference can be naturally captured by the language model used for generating stego texts.
no code implementations • 2 Feb 2021 • Yalan Qin, Guorui Feng, Hanzhou Wu, Yanli Ren, Xinpeng Zhang
With the propogation of the low-rank structure, the corresponding sparsity for representation of original Gabor filter bank can be significantly promoted.
no code implementations • 1 Nov 2020 • Xiangyu Zhao, Hanzhou Wu, Xinpeng Zhang
Many learning tasks require us to deal with graph data which contains rich relational information among elements, leading increasing graph neural network (GNN) models to be deployed in industrial products for improving the quality of service.