22 citations · 34 across the 3 of their papers we have counts for
5 papers
Identity-Driven DeepFake Detection
Xiaoyi Dong, Jianmin Bao, Dongdong Chen +5
DeepFake detection has so far been dominated by ``artifact-driven'' methods and the detection performance significantly degrades when either the type of image artifacts is unknown…
LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
Hang Zhou, Dongdong Chen, Jing Liao +6
Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samp…
GreedyFool: Distortion-Aware Sparse Adversarial Attack
Xiaoyi Dong, Dongdong Chen, Jianmin Bao +5
Modern deep neural networks(DNNs) are vulnerable to adversarial samples. Sparse adversarial samples are a special branch of adversarial samples that can fool the target model by on…
Once a MAN: Towards Multi-Target Attack via Learning Multi-Target Adversarial Network Once
Jiangfan Han, Xiaoyi Dong, Ruimao Zhang +5
Modern deep neural networks are often vulnerable to adversarial samples. Based on the first optimization-based attacking method, many following methods are proposed to improve the…
CAAD 2018: Powerful None-Access Black-Box Attack Based on Adversarial Transformation Network
Xiaoyi Dong, Weiming Zhang, Nenghai Yu
In this paper, we propose an improvement of Adversarial Transformation Networks(ATN) to generate adversarial examples, which can fool white-box models and black-box models with a s…