Yi Zeng

Orcid: 0000-0002-6901-9194

Affiliations:

Virginia Tech, Blacksburg, VA, USA
Xidian University, State Key Laboratory of Integrated Service Networks, Xi'an, China

According to our database¹, Yi Zeng authored at least 44 papers between 2018 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

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In proceedings

Article

PhD thesis

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Other

Bibliography

2024

An Efficient Preprocessing-Based Approach to Mitigate Advanced Adversarial Attacks.

[BibT_eX]

[DOI]

IEEE Trans. Computers, March, 2024

AIR-Bench 2024: A Safety Benchmark Based on Risk Categories from Regulations and Policies.

[BibT_eX]

[DOI]

CoRR, 2024

AI Risk Categorization Decoded (AIR 2024): From Government Regulations to Corporate Policies.

[BibT_eX]

[DOI]

CoRR, 2024

SORRY-Bench: Systematically Evaluating Large Language Model Safety Refusal Behaviors.

[BibT_eX]

[DOI]

Udari Madhushani Sehwag

CoRR, 2024

Fairness-Aware Meta-Learning via Nash Bargaining.

[BibT_eX]

[DOI]

Yi Zeng

Xuelin Yang

Li Chen

Cristian Canton Ferrer

Ming Jin

Michael I. Jordan

Ruoxi Jia

CoRR, 2024

JIGMARK: A Black-Box Approach for Enhancing Image Watermarks against Diffusion Model Edits.

[BibT_eX]

[DOI]

CoRR, 2024

AI Risk Management Should Incorporate Both Safety and Security.

[BibT_eX]

[DOI]

CoRR, 2024

Introducing v0.5 of the AI Safety Benchmark from MLCommons.

[BibT_eX]

[DOI]

Borhane Blili-Hamelin

Kurt D. Bollacker

Rishi Bomassani

Marisa Ferrara Boston

Zacharie Delpierre Coudert

Joseph Marvin Imperial

Dinesh Jinenhally Naganna

Forough Poursabzi-Sangdeh

Alice Schoenauer Sebag

Elizabeth Anne Watkins

CoRR, 2024

A Safe Harbor for AI Evaluation and Red Teaming.

[BibT_eX]

[DOI]

CoRR, 2024

RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Position: A Safe Harbor for AI Evaluation and Red Teaming.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!

[BibT_eX]

[DOI]

Proceedings of the Twelfth International Conference on Learning Representations, 2024

BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models.

[BibT_eX]

[DOI]

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs.

[BibT_eX]

[DOI]

Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

2023

Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2023

Who Leaked the Model? Tracking IP Infringers in Accountable Federated Learning.

[BibT_eX]

[DOI]

CoRR, 2023

Alteration-free and Model-agnostic Origin Attribution of Generated Images.

[BibT_eX]

[DOI]

CoRR, 2023

LAVA: Data Valuation without Pre-Specified Learning Algorithms.

[BibT_eX]

[DOI]

CoRR, 2023

Meta-Sift: How to Sift Out a Clean Subset in the Presence of Data Poisoning?

[BibT_eX]

[DOI]

Proceedings of the 32nd USENIX Security Symposium, 2023

ASSET: Robust Backdoor Data Detection Across a Multiplicity of Deep Learning Paradigms.

[BibT_eX]

[DOI]

Proceedings of the 32nd USENIX Security Symposium, 2023

Where Did I Come From? Origin Attribution of AI-Generated Images.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Revisiting Data-Free Knowledge Distillation with Poisoned Teachers.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

Towards Robustness Certification Against Universal Perturbations.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

LAVA: Data Valuation without Pre-Specified Learning Algorithms.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Narcissus: A Practical Clean-Label Backdoor Attack with Limited Information.

[BibT_eX]

[DOI]

Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, 2023

2022

How to Sift Out a Clean Data Subset in the Presence of Data Poisoning?

[BibT_eX]

[DOI]

CoRR, 2022

CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Adversarial Unlearning of Backdoors via Implicit Hypergradient.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

A Unified Framework for Task-Driven Data Quality Management.

[BibT_eX]

[DOI]

CoRR, 2021

Fine-tuning Is Not Enough: A Simple yet Effective Watermark Removal Attack for DNN Models.

[BibT_eX]

[DOI]

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021

Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective.

[BibT_eX]

[DOI]

Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

DeepSweep: An Evaluation Framework for Mitigating DNN Backdoor Attacks using Data Augmentation.

[BibT_eX]

[DOI]

Bhavani Thuraisingham

Proceedings of the ASIA CCS '21: ACM Asia Conference on Computer and Communications Security, 2021

2020

Optimizing energy and spectrum efficiency of virtual optical network embedding in elastic optical networks.

[BibT_eX]

[DOI]

Opt. Switch. Netw., 2020

FenceBox: A Platform for Defeating Adversarial Examples with Data Augmentation Techniques.

[BibT_eX]

[DOI]

CoRR, 2020

The Hidden Vulnerability of Watermarking for Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Mitigating Advanced Adversarial Attacks with More Advanced Gradient Obfuscation Techniques.

[BibT_eX]

[DOI]

CoRR, 2020

A Data Augmentation-Based Defense Method Against Adversarial Attacks in Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the Algorithms and Architectures for Parallel Processing, 2020

2019

TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction.

[BibT_eX]

[DOI]

CoRR, 2019

$Deep-Full-Range$ : A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework.

[BibT_eX]

[DOI]

IEEE Access, 2019

Using Adversarial Examples to Bypass Deep Learning Based URL Detection System.

[BibT_eX]

[DOI]

Wencheng Chen

Yi Zeng

Meikang Qiu

Proceedings of the IEEE International Conference on Smart Cloud, 2019

Joint Energy and Spectrum Efficient Virtual Optical Network embedding in EONs.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Conference on High Performance Switching and Routing, 2019

V-PSC: A Perturbation-Based Causative Attack Against DL Classifiers' Supply Chain in VANET.

[BibT_eX]

[DOI]

Proceedings of the 2019 IEEE International Conference on Computational Science and Engineering, 2019

DeepVCM: A Deep Learning Based Intrusion Detection Method in VANET.

[BibT_eX]

[DOI]

Proceedings of the 5th IEEE International Conference on Big Data Security on Cloud, 2019

2018

Senior2Local: A Machine Learning Based Intrusion Detection Method for VANETs.

[BibT_eX]

[DOI]

Proceedings of the Smart Computing and Communication - Third International Conference, 2018

Yi Zeng

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