output
20072026
most citedA Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges

2.7k citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing 2019 · cs.CRShow all

6 papers · 2 filters

cs.CR2019★ 3 cited

IStego100K: Large-scale Image Steganalysis Dataset

Zhongliang Yang, Ke Wang, Sai Ma +3

In order to promote the rapid development of image steganalysis technology, in this paper, we construct and release a multivariable large-scale image steganalysis dataset called IS…

cs.CR2019★ 11 cited

Towards Security Threats of Deep Learning Systems: A Survey

Yingzhe He, Guozhu Meng, Kai Chen +2

Deep learning has gained tremendous success and great popularity in the past few years. However, deep learning systems are suffering several inherent weaknesses, which can threaten…

cs.CR2019★ 25 cited

StrongChain: Transparent and Collaborative Proof-of-Work Consensus

Pawel Szalachowski, Daniel Reijsbergen, Ivan Homoliak +1

Bitcoin is the most successful cryptocurrency so far. This is mainly due to its novel consensus algorithm, which is based on proof-of-work combined with a cryptographically-protect…

cs.CR2019★ 2 cited

Information Leakage in Encrypted Deduplication via Frequency Analysis: Attacks and Defenses

Jingwei Li, Patrick P. C. Lee, Chufeng Tan +2

Encrypted deduplication combines encryption and deduplication to simultaneously achieve both data security and storage efficiency. State-of-the-art encrypted deduplication systems…

cs.CR2019★ 225 cited

Security and Privacy on Blockchain

Rui Zhang, Rui Xue, Ling Liu

Blockchain offers an innovative approach to storing information, executing transactions, performing functions, and establishing trust in an open environment. Many consider blockcha…

cs.CR2019★ 7 cited

Zipper Stack: Shadow Stacks Without Shadow

Jinfeng Li, Liwei Chen, Qizhen Xu +4

Return-Oriented Programming (ROP) is a typical attack technique that exploits return addresses to abuse existing code repeatedly. Most of the current return address protecting mech…