An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain-Reference-Cited by-同舟云学术

An Empirical Study of Artifacts and Security Risks in the Pre-trained Model Supply Chain

Published:2022-11-08 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2022 ACM Workshop on Software Supply Chain Offensive Research and Ecosystem Defenses
language:
Short-container-title:

Author:

Jiang Wenxin¹,Synovic Nicholas²,Sethi Rohan²,Indarapu Aryan³,Hyatt Matt²,Schorlemmer Taylor R.¹,Thiruvathukal George K.²,Davis James C.¹

Affiliation:

1. Purdue University, West Lafayette, IN, USA

2. Loyola University Chicago, Chicago, IL, USA

3. University of Illinois-Urbana Champaign, Champaign, IL, USA

Funder

Cisco

Google

NSF (National Science Foundation)

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3560835.3564547

Reference88 articles.

1. Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey

2. Vishnu Banna , Akhil Chinnakotla , Zhengxin Yan , Anirudh Vegesana , Naveen Vivek , Kruthi Krishnappa , Wenxin Jiang , Yung-Hsiang Lu , George K. Thiruvathukal , and James C . Davis . 2021 . An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors . http://arxiv.org/abs/2107.00821 Vishnu Banna, Akhil Chinnakotla, Zhengxin Yan, Anirudh Vegesana, Naveen Vivek, Kruthi Krishnappa, Wenxin Jiang, Yung-Hsiang Lu, George K. Thiruvathukal, and James C. Davis. 2021. An Experience Report on Machine Learning Reproducibility: Guidance for Practitioners and TensorFlow Model Garden Contributors. http://arxiv.org/abs/2107.00821

3. Adrien Bibal and Benoît Frénay . 2016 . Interpretability of Machine Learning Models and Representations: an Introduction . In European Symposium on Artificial Neural Networks. Adrien Bibal and Benoît Frénay. 2016. Interpretability of Machine Learning Models and Representations: an Introduction. In European Symposium on Artificial Neural Networks.

4. On testing machine learning programs

5. Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay and Debdeep Mukhopadhyay. 2018. Adversarial Attacks and Defences: A Survey. https://arxiv.org/abs/1810.00069 Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay and Debdeep Mukhopadhyay. 2018. Adversarial Attacks and Defences: A Survey. https://arxiv.org/abs/1810.00069

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Challenges and practices of deep learning model reengineering: A case study on computer vision;Empirical Software Engineering;2024-08-20

2. Supply Chain Security, Technological Advancements, and Future Trends;Advances in Information Security, Privacy, and Ethics;2024-06-30

3. BELT: Old-School Backdoor Attacks can Evade the State-of-the-Art Defense with Backdoor Exclusivity Lifting;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

4. Signing in Four Public Software Package Registries: Quantity, Quality, and Influencing Factors;2024 IEEE Symposium on Security and Privacy (SP);2024-05-19

5. PeaTMOSS: A Dataset and Initial Analysis of Pre-Trained Models in Open-Source Software;Proceedings of the 21st International Conference on Mining Software Repositories;2024-04-15