Emily Wenger

Orcid: 0009-0006-3346-8226

According to our database¹, Emily Wenger authored at least 34 papers between 2020 and 2026.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

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Book In proceedings Article PhD thesis Dataset Other

Bibliography

2026

Identifying AI Web Scrapers Using Canary Tokens.

[BibT_eX]

[DOI]

CoRR, May, 2026

Improving ML Attacks on LWE with Data Repetition and Stepwise Regression.

[BibT_eX]

[DOI]

CoRR, April, 2026

2025

Scrapers selectively respect robots.txt directives: evidence from a large-scale empirical study.

[BibT_eX]

[DOI]

CoRR, May, 2025

What happens when generative AI models train recursively on each others' generated outputs?

[BibT_eX]

[DOI]

Hung Ahn Vu

Galen Reeves

Emily Wenger

CoRR, May, 2025

Exploring Causes of Representational Similarity in Machine Learning Models.

[BibT_eX]

[DOI]

CoRR, May, 2025

We're Different, We're the Same: Creative Homogeneity Across LLMs.

[BibT_eX]

[DOI]

Emily Wenger

Yoed N. Kenett

CoRR, January, 2025

Salsa Fresca: Angular Embeddings and Pre-Training for ML Attacks on Learning With Errors.

[BibT_eX]

[DOI]

Trans. Mach. Learn. Res., 2025

Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic.

[BibT_eX]

[DOI]

IACR Cryptol. ePrint Arch., 2025

Benchmarking Attacks on Learning with Errors.

[BibT_eX]

[DOI]

Proceedings of the IEEE Symposium on Security and Privacy, 2025

TAPAS: Datasets for Learning the Learning with Errors Problem.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2025, 2025

Scrapers Selectively Respect robots.txt Directives: Evidence From a Large-Scale Empirical Study.

[BibT_eX]

[DOI]

Proceedings of the 2025 ACM Internet Measurement Conference, 2025

Making Hard Problems Easier with Custom Data Distributions and Loss Regularization: A Case Study in Modular Arithmetic.

[BibT_eX]

[DOI]

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Data Isotopes for Data Provenance in DNNs.

[BibT_eX]

[DOI]

Proc. Priv. Enhancing Technol., January, 2024

Teaching Transformers Modular Arithmetic at Scale.

[BibT_eX]

[DOI]

CoRR, 2024

The Cool and the Cruel: Separating Hard Parts of LWE Secrets.

[BibT_eX]

[DOI]

Proceedings of the Progress in Cryptology - AFRICACRYPT 2024, 2024

2023

SALSA PICANTE: a machine learning attack on LWE with binary secrets.

[BibT_eX]

[DOI]

IACR Cryptol. ePrint Arch., 2023

SALSA VERDE: a machine learning attack on Learning with Errors with sparse small secrets.

[BibT_eX]

[DOI]

IACR Cryptol. ePrint Arch., 2023

Glaze: Protecting Artists from Style Mimicry by Text-to-Image Models.

[BibT_eX]

[DOI]

Proceedings of the 32nd USENIX Security Symposium, 2023

SoK: Anti-Facial Recognition Technology.

[BibT_eX]

[DOI]

Proceedings of the 44th IEEE Symposium on Security and Privacy, 2023

SALSA VERDE: a machine learning attack on LWE with sparse small secrets.

[BibT_eX]

[DOI]

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

SalsaPicante: A Machine Learning Attack on LWE with Binary Secrets.

[BibT_eX]

[DOI]

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

2022

Natural Backdoor Datasets.

[BibT_eX]

[DOI]

CoRR, 2022

Assessing Privacy Risks from Feature Vector Reconstruction Attacks.

[BibT_eX]

[DOI]

CoRR, 2022

Blacklight: Scalable Defense for Neural Networks against Query-Based Black-Box Attacks.

[BibT_eX]

[DOI]

Proceedings of the 31st USENIX Security Symposium, 2022

SALSA: Attacking Lattice Cryptography with Transformers.

[BibT_eX]

[DOI]

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

Finding Naturally Occurring Physical Backdoors in Image Datasets.

[BibT_eX]

[DOI]

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

Post-breach Recovery: Protection against White-box Adversarial Examples for Leaked DNN Models.

[BibT_eX]

[DOI]

Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security, 2022

2021

Backdoor Attacks Against Deep Learning Systems in the Physical World.

[BibT_eX]

[DOI]

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real World.

[BibT_eX]

[DOI]

Proceedings of the CCS '21: 2021 ACM SIGSAC Conference on Computer and Communications Security, Virtual Event, Republic of Korea, November 15, 2021

2020

Backdoor Attacks on Facial Recognition in the Physical World.

[BibT_eX]

[DOI]

CoRR, 2020

Blacklight: Defending Black-Box Adversarial Attacks on Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2020

Fawkes: Protecting Personal Privacy against Unauthorized Deep Learning Models.

[BibT_eX]

[DOI]

CoRR, 2020

Fawkes: Protecting Privacy against Unauthorized Deep Learning Models.

[BibT_eX]

[DOI]

Proceedings of the 29th USENIX Security Symposium, 2020

Gotta Catch'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks.

[BibT_eX]

[DOI]

Proceedings of the CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020

Emily Wenger

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