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Shucen Liu

dblp:318/9645 · DBLP profile ↗
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2ranked-venue papers
1as first author
2since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Web and mobile security · 70% Systems and software security · 30%

Topics — the 3 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Web and mobile security › mobile security
android application security
0.612022
Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit · WWW 2022
Web and mobile security
mobile security
0.612022
Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit · WWW 2022
Systems and software security
vulnerability discovery
0.612022
Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit · WWW 2022

Methods — techniques the papers use, named apart from their topics

static analysis · 0.6dynamic verification · 0.6
YearPublicationVenuePosition
2024 Fast CU partition algorithm based on swin-transformer for depth intra coding in 3D-HEVC
Shucen Liu, Shaoguo Cui, Tiansong Li, Haokun Liu, Qingsong Yang
Multim. Tools Appl.1
2022 Lie to Me: Abusing the Mobile Content Sharing Service for Fun and Profit
abstract
Online content sharing is a widely used feature in Android apps. In this paper, we observe a new Fake-Share attack that adversaries can abuse existing content sharing services to manipulate the displayed source of shared content to bypass the content review of targeted Online Social Apps (OSAs) and induce users to click on the shared fraudulent content. We show that seven popular content-sharing services (including WeChat, AliPay, and KakaoTalk) are vulnerable to such an attack. To detect this kind of attack and explore whether adversaries have leveraged it in the wild, we propose DeFash, a multi-granularity detection tool including static analysis and dynamic verification. The extensive in-the-lab and in-the-wild experiments demonstrate that DeFash is effective in detecting such attacks. We have identified 51 real-world apps involved in Fake-Share attacks. We have further harvested over 24K Sharing Identification Information (SIIs) that can be abused by attackers. It is hence urgent for our community to take actions to detect and mitigate this kind of attack.
Guosheng Xu 0001, Hao Zhou 0043, Shucen Liu, Yutian Tang, Li Li 0029, Xiapu Luo, Xusheng Xiao, Guoai Xu, Haoyu Wang 0001
WWW4