Search Results for author: Shichang Sun

Found 6 papers, 0 papers with code

Detect and remove watermark in deep neural networks via generative adversarial networks

no code implementations15 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.

Protecting the Intellectual Properties of Deep Neural Networks with an Additional Class and Steganographic Images

no code implementations19 Apr 2021 Shichang Sun, Mingfu Xue, Jian Wang, Weiqiang Liu

To address these challenges, in this paper, we propose a method to protect the intellectual properties of DNN models by using an additional class and steganographic images.

Image Steganography Management

Robust Backdoor Attacks against Deep Neural Networks in Real Physical World

no code implementations15 Apr 2021 Mingfu Xue, Can He, Shichang Sun, Jian Wang, Weiqiang Liu

In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world.

Backdoor Attack Face Recognition

ActiveGuard: An Active DNN IP Protection Technique via Adversarial Examples

no code implementations2 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.

Management

SocialGuard: An Adversarial Example Based Privacy-Preserving Technique for Social Images

no code implementations27 Nov 2020 Mingfu Xue, Shichang Sun, Zhiyu Wu, Can He, Jian Wang, Weiqiang Liu

After being injected with the perturbation, the social image can easily fool the object detector, while its visual quality will not be degraded.

Object Privacy Preserving

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