1 code implementation • 23 Feb 2024 • Shuren Qi, Yushu Zhang, Chao Wang, Zhihua Xia, Xiaochun Cao, Jian Weng
Developing robust and interpretable vision systems is a crucial step towards trustworthy artificial intelligence.
no code implementations • 15 Oct 2023 • Mingfu Xue, Leo Yu Zhang, Yushu Zhang, Weiqiang Liu
In this review, we attempt to clearly elaborate on the connotation, attributes, and requirements of active DNN copyright protection, provide evaluation methods and metrics for active copyright protection, review and analyze existing work on active DL model intellectual property protection, discuss potential attacks that active DL model copyright protection techniques may face, and provide challenges and future directions for active DL model intellectual property protection.
no code implementations • 2 Jul 2023 • Tao Wang, Yushu Zhang, Zixuan Yang, Hua Zhang, Zhongyun Hua
Massive captured face images are stored in the database for the identification of individuals.
1 code implementation • 18 May 2023 • Chao Wang, Shuren Qi, Zhiqiu Huang, Yushu Zhang, Rushi Lan, Xiaochun Cao
It expands the above works on two aspects: 1) the introduced Krawtchouk basis provides better spatial-frequency discriminability and thereby is more suitable for capturing adversarial patterns than the common trigonometric or wavelet basis; 2) the extensive parameters for decomposition are generated by a pseudo-random function with secret keys, hence blocking the defense-aware adversarial attack.
1 code implementation • 18 Jan 2023 • Shuren Qi, Yushu Zhang, Chao Wang, Tao Xiang, Xiaochun Cao, Yong Xiang
In this paper, we explore a non-learning paradigm that aims to derive robust representation directly from noisy images, without the denoising as pre-processing.
no code implementations • 14 Oct 2022 • Mingfu Xue, Xin Wang, Yinghao Wu, Shifeng Ni, Yushu Zhang, Weiqiang Liu
Since the intrinsic feature is composed of unique interpretation of the model's decision, the intrinsic feature can be regarded as fingerprint of the model.
no code implementations • 5 Sep 2022 • Kuiyuan Zhang, Zhongyun Hua, Yuanman Li, Yushu Zhang, Yicong Zhou
We develop a projection-based transformer block by integrating the prior projection knowledge of CS into the original transformer blocks, and then build a symmetrical reconstruction model using the projection-based transformer blocks and residual convolutional blocks.
1 code implementation • 19 Jul 2022 • Chao Wang, Zhiqiu Huang, Shuren Qi, Yaoshen Yu, Guohua Shen, Yushu Zhang
In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation.
no code implementations • 23 Apr 2022 • Yushu Zhang, Nuo Chen, Shuren Qi, Mingfu Xue, Xiaochun Cao
In this paper, we try to explore a solution from the perspective of the spatial correlation, which exhibits the generic detection capability for both conventional and deep learning-based recoloring.
1 code implementation • 2 Mar 2022 • Shuren Qi, Yushu Zhang, Chao Wang, Jiantao Zhou, Xiaochun Cao
Image forensics is a rising topic as the trustworthy multimedia content is critical for modern society.
no code implementations • 31 Jan 2022 • Mingfu Xue, Shifeng Ni, Yinghao Wu, Yushu Zhang, Jian Wang, Weiqiang Liu
Recent researches demonstrate that Deep Neural Networks (DNN) models are vulnerable to backdoor attacks.
no code implementations • 3 Jan 2022 • Mingfu Xue, Xin Wang, Shichang Sun, Yushu Zhang, Jian Wang, Weiqiang Liu
After training, the backdoor attack against DNN is robust to image compression.
no code implementations • 15 Jun 2021 • Haoqi Wang, Mingfu Xue, Shichang Sun, Yushu Zhang, Jian Wang, Weiqiang Liu
Experimental evaluations on the MNIST and CIFAR10 datasets demonstrate that, the proposed method can effectively remove about 98% of the watermark in DNN models, as the watermark retention rate reduces from 100% to less than 2% after applying the proposed attack.
no code implementations • 29 May 2021 • Mingfu Xue, Yinghao Wu, Zhiyu Wu, Yushu Zhang, Jian Wang, Weiqiang Liu
Experimental results show that, the backdoor detection rate of the proposed defense method is 99. 63%, 99. 76% and 99. 91% on Fashion-MNIST, CIFAR-10 and GTSRB datasets, respectively.
no code implementations • 28 May 2021 • Mingfu Xue, Zhiyu Wu, Jian Wang, Yushu Zhang, Weiqiang Liu
Moreover, the proposed method only needs to encrypt an extremely low number of parameters, and the proportion of the encrypted parameters of all the model's parameters is as low as 0. 000205%.
1 code implementation • 27 Mar 2021 • Shuren Qi, Yushu Zhang, Chao Wang, Jiantao Zhou, Xiaochun Cao
Image representation is an important topic in computer vision and pattern recognition.
no code implementations • 2 Mar 2021 • Mingfu Xue, Shichang Sun, Can He, Yushu Zhang, Jian Wang, Weiqiang Liu
For ownership verification, the embedded watermark can be successfully extracted, while the normal performance of the DNN model will not be affected.
no code implementations • 5 Nov 2020 • Wenying Wen, Rongxin Tu, Yushu Zhang, Yuming Fang, Yong Yang
High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption.
no code implementations • 12 Jun 2019 • Chiranjibi Sitaula, Yong Xiang, Yushu Zhang, Xuequan Lu, Sunil Aryal
Nevertheless, most of the existing feature extraction methods, which extract features based on pixels, color, shape/object parts or objects on images, suffer from limited capabilities in describing semantic information (e. g., object association).