333 citations · 1.3k across the 71 of their papers we have counts for
10 papers · 1 filter
TrojanZoo: Towards Unified, Holistic, and Practical Evaluation of Neural Backdoors
Ren Pang, Zheng Zhang, Xiangshan Gao +5
Neural backdoors represent one primary threat to the security of deep learning systems. The intensive research has produced a plethora of backdoor attacks/defenses, resulting in a…
Practical Privacy Attacks on Vertical Federated Learning
Haiqin Weng, Juntao Zhang, Xingjun Ma +4
Federated learning (FL) is a privacy-preserving learning paradigm that allows multiple parities to jointly train a powerful machine learning model without sharing their private dat…
UNIFUZZ: A Holistic and Pragmatic Metrics-Driven Platform for Evaluating Fuzzers
Yuwei Li, Shouling Ji, Yuan Chen +9
A flurry of fuzzing tools (fuzzers) have been proposed in the literature, aiming at detecting software vulnerabilities effectively and efficiently. To date, it is however still cha…
Unsupervised Reference-Free Summary Quality Evaluation via Contrastive Learning
Hanlu Wu, Tengfei Ma, Lingfei Wu +2
Evaluation of a document summarization system has been a critical factor to impact the success of the summarization task. Previous approaches, such as ROUGE, mainly consider the in…
Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing
Hanlu Wu, Tengfei Ma, Lingfei Wu +1
Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-ex…
Deep Graph Matching and Searching for Semantic Code Retrieval
Xiang Ling, Lingfei Wu, Saizhuo Wang +6
Code retrieval is to find the code snippet from a large corpus of source code repositories that highly matches the query of natural language description. Recent work mainly uses na…
Multilevel Graph Matching Networks for Deep Graph Similarity Learning
Xiang Ling, Lingfei Wu, Saizhuo Wang +5
While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph…
AdvMind: Inferring Adversary Intent of Black-Box Attacks
Ren Pang, Xinyang Zhang, Shouling Ji +2
Deep neural networks (DNNs) are inherently susceptible to adversarial attacks even under black-box settings, in which the adversary only has query access to the target models. In p…
Graph Backdoor
Zhaohan Xi, Ren Pang, Shouling Ji +1
One intriguing property of deep neural networks (DNNs) is their inherent vulnerability to backdoor attacks -- a trojan model responds to trigger-embedded inputs in a highly predict…
Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network
Zhe Ma, Jianfeng Dong, Yao Zhang +4
This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute am…