11 citations · 32 across the 5 of their papers we have counts for
9 papers
Random Noise Defense Against Query-Based Black-Box Attacks
Zeyu Qin, Yanbo Fan, Hongyuan Zha +1
The query-based black-box attacks have raised serious threats to machine learning models in many real applications. In this work, we study a lightweight defense method, dubbed Rand…
Dual ResGCN for Balanced Scene GraphGeneration
Jingyi Zhang, Yong Zhang, Baoyuan Wu +3
Visual scene graph generation is a challenging task. Previous works have achieved great progress, but most of them do not explicitly consider the class imbalance issue in scene gra…
Boosting Black-Box Attack with Partially Transferred Conditional Adversarial Distribution
Yan Feng, Baoyuan Wu, Yanbo Fan +3
This work studies black-box adversarial attacks against deep neural networks (DNNs), where the attacker can only access the query feedback returned by the attacked DNN model, while…
Toward Adversarial Robustness via Semi-supervised Robust Training
Yiming Li, Baoyuan Wu, Yan Feng +4
Adversarial examples have been shown to be the severe threat to deep neural networks (DNNs). One of the most effective adversarial defense methods is adversarial training (AT) thro…
Controllable Descendant Face Synthesis
Yong Zhang, Le Li, Zhilei Liu +3
Kinship face synthesis is an interesting topic raised to answer questions like "what will your future children look like?". Published approaches to this topic are limited. Most of…
Exact Adversarial Attack to Image Captioning via Structured Output Learning with Latent Variables
Yan Xu, Baoyuan Wu, Fumin Shen +4
In this work, we study the robustness of a CNN+RNN based image captioning system being subjected to adversarial noises. We propose to fool an image captioning system to generate so…