Cross-Domain Replay Spoofing Attack Detection Using Domain Adversarial Training

2019 ◽  
Author(s):  
Hongji Wang ◽  
Heinrich Dinkel ◽  
Shuai Wang ◽  
Yanmin Qian ◽  
Kai Yu
Keyword(s):  
Attack Detection ◽  
Cross Domain ◽  
Adversarial Training

scholarly journals Show, Adapt and Tell: Adversarial Training of Cross-Domain Image Captioner

2017 ◽  
Author(s):  
Tseng-Hung Chen ◽  
Yuan-Hong Liao ◽  
Ching-Yao Chuang ◽  
Wan-Ting Hsu ◽  
Jianlong Fu ◽  
...  
Keyword(s):  
Cross Domain ◽  
Adversarial Training

Adversarial Cross-domain Community Question Retrieval

2022 ◽  
Vol 21 (3) ◽  
pp. 1-22
Author(s):  
Aibo Guo ◽  
Xinyi Li ◽  
Ning Pang ◽  
Xiang Zhao
Keyword(s):  
Social Media ◽  
Information Sharing ◽  
Information Access ◽  
Professional Knowledge ◽  
Feature Space ◽  
Online Information ◽  
Cross Domain ◽  
Adversarial Training ◽  
Sentence Matching ◽  
Target Data

Community Q&A forum is a special type of social media that provides a platform to raise questions and to answer them (both by forum participants), to facilitate online information sharing. Currently, community Q&A forums in professional domains have attracted a large number of users by offering professional knowledge. To support information access and save users’ efforts of raising new questions, they usually come with a question retrieval function, which retrieves similar existing questions (and their answers) to a user’s query. However, it can be difficult for community Q&A forums to cover all domains, especially those emerging lately with little labeled data but great discrepancy from existing domains. We refer to this scenario as cross-domain question retrieval. To handle the unique challenges of cross-domain question retrieval, we design a model based on adversarial training, namely, X-QR , which consists of two modules—a domain discriminator and a sentence matcher. The domain discriminator aims at aligning the source and target data distributions and unifying the feature space by domain-adversarial training. With the assistance of the domain discriminator, the sentence matcher is able to learn domain-consistent knowledge for the final matching prediction. To the best of our knowledge, this work is among the first to investigate the domain adaption problem of sentence matching for community Q&A forums question retrieval. The experiment results suggest that the proposed X-QR model offers better performance than conventional sentence matching methods in accomplishing cross-domain community Q&A tasks.

scholarly journals Privacy-Preserving Ddos Attack Detection using Cross-Domain: Challenges and Research

2019 ◽  
Vol 9 (1) ◽  
pp. 2373-2382
Keyword(s):  
Intrusion Detection ◽  
Privacy Preserving ◽  
Attack Detection ◽  
Detection Techniques ◽  
Ddos Attack ◽  
Cross Domain ◽  
Ddos Attack Detection

In this paper, we present a review of on hand IDS (Intrusion Detection Techniques) for DDoS assaults. Interruption discovery framework is a well known and computationally costly task. We additionally clarify the essentials of interruption identification framework. We represent the present methodologies for Intrusion Detection framework. From the expansive assortment of proficient procedures that have been created we will look at the most significant ones. Their qualities and shortcomings are likewise researched. For reasons unknown, the conduct of the calculations is substantially more comparative as not out of the ordinary.

scholarly journals VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and Adaptation

2021 ◽  
Author(s):  
Jiong Wang ◽  
Zhou Zhao ◽  
Weike Jin ◽  
Xinyu Duan ◽  
Zhen Lei ◽  
...  
Keyword(s):  
Attack Detection ◽  
Cross Domain ◽  
Face Presentation ◽  
Presentation Attack Detection

scholarly journals Ensemble of Multi-View Learning Classifiers for Cross-Domain Iris Presentation Attack Detection

2019 ◽  
Vol 14 (6) ◽  
pp. 1419-1431 ◽  
Author(s):  
Andrey Kuehlkamp ◽  
Allan Pinto ◽  
Anderson Rocha ◽  
Kevin W. Bowyer ◽  
Adam Czajka
Keyword(s):  
Attack Detection ◽  
Cross Domain ◽  
Learning Classifiers ◽  
Presentation Attack Detection

Semantic-aware short path adversarial training for cross-domain semantic segmentation

2020 ◽  
Vol 380 ◽  
pp. 125-132
Author(s):  
Yuhu Shan ◽  
Chee Meng Chew ◽  
Wen Feng Lu
Keyword(s):  
Short Path ◽  
Semantic Segmentation ◽  
Cross Domain ◽  
Adversarial Training

scholarly journals AlignFlow: Cycle Consistent Learning from Multiple Domains via Normalizing Flows

2020 ◽  
Vol 34 (04) ◽  
pp. 4028-4035 ◽  
Author(s):  
Aditya Grover ◽  
Christopher Chute ◽  
Rui Shu ◽  
Zhangjie Cao ◽  
Stefano Ermon
Keyword(s):  
Domain Adaptation ◽  
Likelihood Estimation ◽  
Learning Objectives ◽  
Target Domain ◽  
Modeling Framework ◽  
Source Domain ◽  
Cross Domain ◽  
Generative Modeling ◽  
Adversarial Training ◽  
Multiple Domains

Given datasets from multiple domains, a key challenge is to efficiently exploit these data sources for modeling a target domain. Variants of this problem have been studied in many contexts, such as cross-domain translation and domain adaptation. We propose AlignFlow, a generative modeling framework that models each domain via a normalizing flow. The use of normalizing flows allows for a) flexibility in specifying learning objectives via adversarial training, maximum likelihood estimation, or a hybrid of the two methods; and b) learning and exact inference of a shared representation in the latent space of the generative model. We derive a uniform set of conditions under which AlignFlow is marginally-consistent for the different learning objectives. Furthermore, we show that AlignFlow guarantees exact cycle consistency in mapping datapoints from a source domain to target and back to the source domain. Empirically, AlignFlow outperforms relevant baselines on image-to-image translation and unsupervised domain adaptation and can be used to simultaneously interpolate across the various domains using the learned representation.

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