UROP Research Mentor Project Submission Portal: Submission #997

Submission information
Submission Number: 997
Submission ID: 19401
Submission UUID: 69120499-ab60-4322-8200-29ef3d95394c

Created: Mon, 04/21/2025 - 02:34 PM
Completed: Mon, 04/21/2025 - 02:34 PM
Changed: Tue, 08/26/2025 - 09:02 PM

Remote IP address: 71.229.11.109
Submitted by: Anonymous
Language: English

Is draft: No
Primary Research Mentor Name Yushun Dong
Research Mentor Preferred Pronouns
When potential research assistants are reaching out via email, what is your preferred honorific?
Contact Email (FSU Email if affiliated) yd24f@fsu.edu
Position Title Faculty
FSU College (if applicable) Arts and Sciences
FSU Department or Non-FSU Organization Affiliation Computer Science Department
Headshot (optional) Profile-2-1.JPG
Research Assistant Supervisor (if different from above)
Research Assistant Supervisor Preferred Pronouns
Research Assistant Supervisor Preferred Honorific?
Contact Email (FSU Email if affiliated)
Name of Other Faculty/Collaborator(s) (if applicable)
Other Faculty/Collaborator(s) Preferred Pronouns
Other Faculty/Collaborator(s) Preferred Honorific?
Contact Email (FSU Email if affiliated)
Title of the Project Model Extraction Attack and Defense
Project Keywords Deep learning, artificial intelligence, model extraction attack, large language models (LLMs), graph learning
Are you currently looking for research assistants? Yes
Number of Research Assistants Needed 6
Relevant Research Assistant Major(s) Open to all majors
Project Location: On FSU Main Campus
If the project location is off campus, does the research assistant(s) need to provide their own transportation? No, the project is remote
Please select the choice that most accurately describes your expectations for the research assistant(s): Partially Remote
Approximately how many hours a week would the research assistant(s) need to work? 10
Roughly what time frame do you expect research assistant(s) to work? Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Overall Research Project Description Machine learning models are becoming a key part of many everyday applications—from search engines and virtual assistants to healthcare and banking. However, as these models become more powerful and widely used, they also become more attractive targets for attackers. One serious threat is called a model extraction attack, where an outsider tries to “steal” a trained model by sending queries and analyzing the outputs. This stolen model can then be misused, duplicated, or reverse-engineered, leading to intellectual property theft, loss of competitive advantage, and serious privacy risks.

This research project focuses on understanding how these attacks work and developing effective defenses. We want to study how attackers interact with machine learning services (often offered as APIs) and figure out what information they can extract. Then, we will design and test various protective strategies to make models more resistant—without significantly lowering their accuracy or slowing them down for legitimate users.

This project is a great opportunity for students interested in cybersecurity, artificial intelligence, or the ethical and legal implications of new technologies. No matter your background, if you’re curious about how smart systems can be tricked—and how to make them safer—this research will give you hands-on experience at the frontier of AI security.
Research Tasks Students joining this project will help review existing work in model extraction and defense to build a strong foundation of knowledge. This will include reading papers and summarizing techniques and trends in a collaborative way, with support from the lead researcher. Together, we’ll identify gaps in the literature where new ideas or experiments can contribute to the field.

You will also assist in developing experiments using open-source machine learning models. This may involve training simple models, simulating extraction attacks, and evaluating how well different defenses perform. Depending on interest and skills, you may help write code for data collection, modify algorithms, or visualize attack/defense results in easy-to-understand formats.

Finally, we will document our findings and prepare materials for future presentations and publications. Students will be encouraged to contribute ideas and, if desired, co-author posters or papers. This is an interactive project where your contributions will directly shape how we understand and improve model security.
Skills that research assistant(s) may need: Required: Basic programming experience, ideally in Python. Familiarity with tools such as Jupyter Notebook, Google Colab, or similar platforms is important since most of our experiments will be coded and tested there.

Recommended: Interest in or prior exposure to machine learning concepts (e.g., through a course or self-study). Experience with libraries like scikit-learn, PyTorch, or TensorFlow will be helpful but not mandatory—training will be provided.

Recommended: Critical thinking and clear communication skills. Because we are working in a fast-evolving and interdisciplinary area, students who can ask thoughtful questions and explain technical concepts in plain language will thrive. All majors are welcome, and diverse perspectives are encouraged.
Mentoring Philosophy My mentoring philosophy centers on cultivating a supportive, collaborative, and intellectually stimulating environment where students are encouraged to explore challenging research questions with curiosity and confidence. I believe in tailoring mentorship to each student's strengths and goals, providing the right balance of guidance and independence to help them grow as researchers and thinkers. Through clear communication, hands-on learning, and regular feedback, I aim to empower students to take ownership of their work and develop both technical and critical reasoning skills. In previous years, this approach has led to successful outcomes, including high-quality publications co-authored with undergraduate researchers through the UROP program.
Please provide a link to your publications, a video clip, or a website for your research project (if applicable):
Please add any additional information here (if applicable):
Are you interested in participating in the UROP Research Mentor Roundtable? Yes
Roundtable times and Zoom links Tuesday Sept. 3, 1 PM - 1:30 PM ET
https://fsu.zoom.us/j/7153751215

Publication records: https://scholar.google.com/citations?hl=en&user=_QUhuOMAAAAJ
Roundtable Info
  • Day: Tuesday, September 2
    Start Time: 1:00
    End Time: 2:00
    Zoom Link: https://fsu.zoom.us/j/7153751215
Mentor Handbook, FAQs, and Communication Yes
UROP Performance Evaluation Yes
Materials Grant Yes
UROP Poster Presentation Yes
Year 2025
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