80 citations · 195 across the 14 of their papers we have counts for
15 papers
Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation
Meixi Zheng, Kehan Wu, Yanbo Fan +2
The transfer-based black-box adversarial attack setting poses the challenge of crafting an adversarial example (AE) on known surrogate models that remain effective against unseen t…
Boosting Backdoor Attack with A Learnable Poisoning Sample Selection Strategy
Zihao Zhu, Mingda Zhang, Shaokui Wei +3
Data-poisoning based backdoor attacks aim to insert backdoor into models by manipulating training datasets without controlling the training process of the target model. Existing at…
Enhancing Fine-Tuning Based Backdoor Defense with Sharpness-Aware Minimization
Mingli Zhu, Shaokui Wei, Li Shen +2
Backdoor defense, which aims to detect or mitigate the effect of malicious triggers introduced by attackers, is becoming increasingly critical for machine learning security and int…
A Large-scale Multiple-objective Method for Black-box Attack against Object Detection
Siyuan Liang, Longkang Li, Yanbo Fan +4
Recent studies have shown that detectors based on deep models are vulnerable to adversarial examples, even in the black-box scenario where the attacker cannot access the model info…
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…
Effective and Robust Detection of Adversarial Examples via Benford-Fourier Coefficients
Chengcheng Ma, Baoyuan Wu, Shibiao Xu +4
Adversarial examples have been well known as a serious threat to deep neural networks (DNNs). In this work, we study the detection of adversarial examples, based on the assumption…
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…
Tencent ML-Images: A Large-Scale Multi-Label Image Database for Visual Representation Learning
Baoyuan Wu, Weidong Chen, Yanbo Fan +4
In existing visual representation learning tasks, deep convolutional neural networks (CNNs) are often trained on images annotated with single tags, such as ImageNet. However, a sin…
Robust Localized Multi-view Subspace Clustering
Yanbo Fan, Jian Liang, Ran He +2
In multi-view clustering, different views may have different confidence levels when learning a consensus representation. Existing methods usually address this by assigning distinct…
Learning with Average Top-k Loss
Yanbo Fan, Siwei Lyu, Yiming Ying +1
In this work, we introduce the {\em average top-} (\atk) loss as a new aggregate loss for supervised learning, which is the average over the largest individual losses over a…
Self-Paced Learning: an Implicit Regularization Perspective
Yanbo Fan, Ran He, Jian Liang +1
Self-paced learning (SPL) mimics the cognitive mechanism of humans and animals that gradually learns from easy to hard samples. One key issue in SPL is to obtain better weighting s…