Search Results for author: Bo Luo

Found 12 papers, 2 papers with code

Hide and Seek: on the Stealthiness of Attacks against Deep Learning Systems

no code implementations31 May 2022 Zeyan Liu, Fengjun Li, Jingqiang Lin, Zhu Li, Bo Luo

In this paper, we present the first large-scale study on the stealthiness of adversarial samples used in the attacks against deep learning.


Dynamic Label Assignment for Object Detection by Combining Predicted IoUs and Anchor IoUs

1 code implementation23 Jan 2022 Tianxiao Zhang, Bo Luo, Ajay Sharda, Guanghui Wang

For anchor-based detection models, the IoU (Intersection over Union) threshold between the anchors and their corresponding ground truth bounding boxes is the key element since the positive samples and negative samples are divided by the IoU threshold.

object-detection Object Detection

Semantic Clustering based Deduction Learning for Image Recognition and Classification

no code implementations25 Dec 2021 Wenchi Ma, Xuemin Tu, Bo Luo, Guanghui Wang

The paper proposes a semantic clustering based deduction learning by mimicking the learning and thinking process of human brains.


Two Souls in an Adversarial Image: Towards Universal Adversarial Example Detection using Multi-view Inconsistency

1 code implementation25 Sep 2021 Sohaib Kiani, Sana Awan, Chao Lan, Fengjun Li, Bo Luo

To this end, Argos first amplifies the discrepancies between the visual content of an image and its misclassified label induced by the attack using a set of regeneration mechanisms and then identifies an image as adversarial if the reproduced views deviate to a preset degree.

DeepDyve: Dynamic Verification for Deep Neural Networks

no code implementations21 Sep 2020 Yu Li, Min Li, Bo Luo, Ye Tian, Qiang Xu

The key to enabling such lightweight checking is that the smaller neural network only needs to produce approximate results for the initial task without sacrificing fault coverage much.

Autonomous Driving

CSRN: Collaborative Sequential Recommendation Networks for News Retrieval

no code implementations7 Apr 2020 Bing Bai, Guanhua Zhang, Ye Lin, Hao Li, Kun Bai, Bo Luo

Recurrent Neural Network (RNN)-based sequential recommendation is a popular approach that utilizes users' recent browsing history to predict future items.

Collaborative Filtering News Retrieval +2

Region-Wise Attack: On Efficient Generation of Robust Physical Adversarial Examples

no code implementations5 Dec 2019 Bo Luo, Qiang Xu

Deep neural networks (DNNs) are shown to be susceptible to adversarial example attacks.

Adversarial Attack

On Functional Test Generation for Deep Neural Network IPs

no code implementations23 Nov 2019 Bo Luo, Yu Li, Lingxiao Wei, Qiang Xu

Considering the large amount of training data and know-how required to generate the network, it is more practical to use third-party DNN intellectual property (IP) cores for many designs.

On Configurable Defense against Adversarial Example Attacks

no code implementations6 Dec 2018 Bo Luo, Min Li, Yu Li, Qiang Xu

Machine learning systems based on deep neural networks (DNNs) have gained mainstream adoption in many applications.

I Know What You See: Power Side-Channel Attack on Convolutional Neural Network Accelerators

no code implementations5 Mar 2018 Lingxiao Wei, Bo Luo, Yu Li, Yannan Liu, Qiang Xu

Deep learning has become the de-facto computational paradigm for various kinds of perception problems, including many privacy-sensitive applications such as online medical image analysis.

Towards Imperceptible and Robust Adversarial Example Attacks against Neural Networks

no code implementations15 Jan 2018 Bo Luo, Yannan Liu, Lingxiao Wei, Qiang Xu

Previous adversarial example crafting methods, however, use simple metrics to evaluate the distances between the original examples and the adversarial ones, which could be easily detected by human eyes.

Learning Social Circles in Ego Networks based on Multi-View Social Graphs

no code implementations16 Jul 2016 Chao Lan, Yuhao Yang, Xiao-Li Li, Bo Luo, Jun Huan

Based on extensive automatic and manual experimental evaluations, we deliver two major findings: first, multi-view clustering techniques perform better than common single-view clustering techniques, which only use one view or naively integrate all views for detection, second, the standard multi-view clustering technique is less robust than our modified technique, which selectively transfers information across views based on an assumption that sparse network structures are (potentially) incomplete.

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