32 citations · 77 across the 10 of their papers we have counts for
10 papers
OmDet: Large-scale vision-language multi-dataset pre-training with multimodal detection network
Tiancheng Zhao, Peng Liu, Kyusong Lee
The advancement of object detection (OD) in open-vocabulary and open-world scenarios is a critical challenge in computer vision. This work introduces OmDet, a novel language-aware…
Multi-Modality Distillation via Learning the teacher's modality-level Gram Matrix
Peng Liu
In the context of multi-modality knowledge distillation research, the existing methods was mainly focus on the problem of only learning teacher final output. Thus, there are still…
Spotting Silent Buffer Overflows in Execution Trace through Graph Neural Network Assisted Data Flow Analysis
Zhilong Wang, Li Yu, Suhang Wang +1
A software vulnerability could be exploited without any visible symptoms. When no source code is available, although such silent program executions could cause very serious damage,…
Security and Privacy for Artificial Intelligence: Opportunities and Challenges
Ayodeji Oseni, Nour Moustafa, Helge Janicke +3
The increased adoption of Artificial Intelligence (AI) presents an opportunity to solve many socio-economic and environmental challenges; however, this cannot happen without securi…
Analyzing the Overhead of Filesystem Protection Using Linux Security Modules
Wenhui Zhang, Trent Jaeger, Peng Liu
Over the years, the complexity of the Linux Security Module (LSM) is keeping increasing, and the count of the authorization hooks is nearly doubled. It is important to provide up-t…
Towards classification parity across cohorts
Aarsh Patel, Rahul Gupta, Mukund Harakere +3
Recently, there has been a lot of interest in ensuring algorithmic fairness in machine learning where the central question is how to prevent sensitive information (e.g. knowledge a…
Practical Verification of MapReduce Computation Integrity via Partial Re-execution
Eunjung Yoon, Peng Liu
Big data processing is often outsourced to powerful, but untrusted cloud service providers that provide agile and scalable computing resources to weaker clients. However, untrusted…
Learning Privately over Distributed Features: An ADMM Sharing Approach
Yaochen Hu, Peng Liu, Linglong Kong +1
Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem…
SvTPM: A Secure and Efficient vTPM in the Cloud
Juan Wang, Chengyang Fan, Jie Wang +5
Virtual Trusted Platform Modules (vTPMs) have been widely used in commercial cloud platforms (e.g. Google Cloud, VMware Cloud, and Microsoft Azure) to provide virtual root-of-trust…
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
Qinglong Wang, Wenbo Guo, Alexander G. Ororbia +6
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of…