695.739.8VL - Generative AI and Synthetic Threats

Expanded Course Description

This course is taught by Yungjun Yoo and Samuel Nathanson.

Course Structure

Course material is divided into weekly modules in Canvas. Each module runs for a period of seven (7) days and contains the following: 

1. Viewing material: virtual live lecture
2. Reading material: assigned publication(s) for reading 
   
3. Lab/Essay assignments. 
   
4. Group Final project.
5. Weekly discussion

Regularly check the upcoming deadlines in the Canvas Calendar, recent Canvas Announcements, Discussion Board for assignment due dates. JHU Academic Calendar is here. 

All dates and times (for assignments, Zoom meetings, etc.) are in ET zone! Each module (a.k.a. "week") starts each week and lasts 1 week. The assignments are released weekly and, typically, have 2 week deadline on Sundays at 11:59 pm ET.

Course Topics

Weekly modules (topics):

1. Introduction to Generative AI in Cybersecurity
   
2. Large Language Models and Disinformation
   
3. Prompt Engineering, Guardrails, and Jailbreaking
4. Project Preparation and Development
5. Generative AI‑induced Social Engineering
6. Synthetic Images, Videos, and Deepfakes
7. Project Work and Midterm Presentations
8. Adversarial Attacks with Data Poisoning
9. Adversarial Attacks on Generative Models
10. Vulnerability Detection and GenAI Malware
11. Ethical Considerations and Legal Implications
12. AI‑Generated Content and Intellectual Property
13. Future Trends and Challenges in Generative AI
14. Final Project Presentations and Mini-Conference

Course Goals

This course aims to equip students with a deep understanding of the latest advancements in Generative AI technologies and their implications in the cybersecurity landscape. By critically analyzing recently published papers from top-tier conferences and journals, students will explore the threats posed by these technologies.
 

Course Learning Outcomes (CLOs)

• Understand generative AI threats from a cybersecurity perspective.
• Learn how generative AI can be applied for defending users from harmful content.
• Obtain an ability to critically assess and discuss current research findings, methodologies, and trends in generative AI.
• Build hands-on skills to explore new techniques in generative AI through the labs
• Develop your own ideas to address cybersecurity problems induced by generative AI.

Textbooks

Required:

• There is no required textbooks for this class
  
• Related publication list will be provided by the instructors

Other Materials & Online Resources

Students need a Google Drive account (available for free with Gmail account) with a Google Colab plugin. Colab is a Jupyter Notebook-like interface, which provides a seamless access to Python, TensorFlow, visualizations and (free) CPU/GPU/TPU hardware from Google. Colab ensures identical programming environment among all participants of the course (students and graders). 

All lectures will be recorded and shared through the Canvas.

Note: Please don't post the copyrighted material in the public space outside of our course; the content takes many hundreds of hours to create and maintain. See JHU's Code of Conduct, Honor Code, and Academic Integrity Policy. Please report suspected cheating incidents to the instructors.

Required Software

We use Google Colab Notebooks for Lab assignments, which is pre-configured with Python 3.x and most packages/versions already installed. Google frequently updates Colab's environment occasionally causing incompatible settings or packages, so please let instructors know if any of this comes up and you notice an error in Colab's code. We'll fix it for everyone.

Student Coursework Requirements

Final grades will be determined by the following weighting:

1. 50%: Class Project (include proposal, midterm presentation, and final presentation)
2. 20%: Lab/Essay Assignments
3. 30%: Discussion posting. 

Grading Policy

Score Range

≥97

≥94

≥90

≥87

≥83

≥80

≥77

≥73

≥70

≥67

≥63

≥0

Letter Grade

A+

A

A-

B+

B

B-

C+

C

C-

D+

D

F

Course Evaluation

This course will consist of the following basic student requirements:

Discussion (30% of Final Grade Calculation)

You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the recently published paper. 

Post your initial response to the discussion questions by the end of day 4 (in EST) for that module week. Posting a response to the discussion question is part one of your grade for module discussions (i.e., Timeliness).

Part two of your grade for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses) with at least 3  additional postings in total to at least 3 other classmates (i.e., Critical Thinking). Just posting your response to a discussion question is not sufficient; we want you to interact with your classmates. Be detailed in your postings and in your responses to your classmates' postings. Feel free to agree or disagree with your classmates. Please ensure that your postings are civil and constructive.

We will monitor module discussions and will respond to some of the discussions as discussions are posted. In some instances, we will summarize the overall discussions and post the summary for the module.

https://docs.google.com/document/d/1Qr9ezd401Swp1LyJRJBz15fCswWlqkjki-pmu1U1T4o/edit?tab=t.0

Evaluation of participation is based on contribution to discussions.

Preparation and participation is evaluated by the following grading elements:

Timeliness (50%)

Critical Thinking (50%)

Preparation and participation is graded as follows:

• 100–90 = A—Timeliness [regularly participates; all required postings; early in discussion; throughout the discussion]; Critical Thinking [rich in content; full of thoughts, insight, and analysis].
• 89–80 = B—Timeliness [frequently participates; all required postings; some not in time for others to read and respond]; Critical Thinking [substantial information; thought, insight, and analysis has taken place].
• 79–70 = C—Timeliness [infrequently participates; all required postings; most at the last minute without allowing for response time]; Critical Thinking [generally competent; information is thin and commonplace].
• <70 = F—Timeliness [rarely participates; some, or all required postings missing]; Critical Thinking [rudimentary and superficial; no analysis or insight is displayed].

Assignments (20% of Final Grade Calculation)

Assignments will include a mix of Lab assignments, and essay assignments. Do not use a cover sheet but include your name and assignment identifier at the first page of your assignment. For the Lab assignment, your code should be readable, easily understandable, and properly commented. Submission of uncompilable code is not acceptable.

All assignments are due according to the dates in the Calendar.

Late submissions will be reduced by 10% of penalty for one day late (no exceptions without prior coordination with the instructors).

Assignments are evaluated by the following grading elements:

1. Each part of question is answered (20%)
2. Writing quality and technical accuracy (30%) (Writing is expected to meet or exceed accepted graduate-level English and scholarship standards. That is, all assignments will be graded on grammar and style as well as content.)
3. Rationale for answer is provided (20%)
4. Examples are included to illustrate rationale (15%) (If you do not have direct experience related to a particular question, then you are to provide analogies versus examples.)
5. Outside references are included (15%)

Assignments are graded as follows:

• 100–90 = A—All parts of question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rich in content; full of thought, insight, and analysis].
• 89–80 = B—All parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [substantial information; thought, insight, and analysis has taken place].
• 79–70=C—Majority of parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [generally competent; information is thin and commonplace].
• <70=F—Some parts of the question are addressed; Writing Quality/ Rationale/ Examples/ Outside References [rudimentary and superficial; no analysis or insight displayed].

Course Project (50% of Final Grade Calculation)

• Each student will complete a group research project over the course of the semester on a topic related to “Generative AI and Synthetic Threats”. The project is divided into three key phases (Proposal, Midterm, and Final). Further details can be found in the official Project Guidelines.

Grading Breakdown

Component	Weight
Project Proposal (Week 4)	10%
Midterm Report & Presentation (Week 7)	15%
Final Paper & Presentation (Week 14)	25%
Total	50%

Grades will reflect both the technical depth and the quality of communication in each phase. The expectations are:

• ·         A (90–100): Clear and well-scoped proposal; strong methodology and progress at midterm; final report shows deep understanding, originality, and strong results; well-prepared presentations with insightful analysis.
• ·         B (80–89): Adequate project design and execution; meaningful progress with some technical depth; presentations and reports are complete but may lack refinement or originality.
• ·         C (70–79): Basic project execution with minimal insight; limited progress or shallow analysis; deliverables meet minimum requirements but lack depth or clarity.
• ·         F (<70): Major components are missing or incomplete; little to no technical insight; poor documentation or lack of engagement with the project.

Special Notes:

If your project topic sparked a deeper interest, we encourage you to consider submitting your results to a relevant journal or conference. You are also welcome to continue developing your idea through a Capstone Project or Independent Study in collaboration with the instructor.

Regrading:

1. We aim to grade fairly, accurately, and timely. If you believe we made a crude grading error, make a private post in our discussion forum ASAP (within 1 week of grading) & tag a grader.
2. To discourage frivolous appeals, we reserve the right to deduct a 2-5% of the grade, if your appeal lacks a strong justification or the benefit fails to exceed 2-5%. Be sure it is worth the mutual effort.
3. If in doubt, always use the course textbook as the main source of truth. It has been well edited, provides full context, and has very few typos.

Plagiarism is a serious offense. Always reference your sources of code and ideas. We encourage learning from StockOverflow, GitHub, Kaggle Discussions, etc., but the key solution and contribution must be your own (and be clearly demonstrated).

1. Rule 0: If ever in doubt, just ask!
2. Every output (plot, table, metrics, ...) must have a purpose and an interpretation.
   1. We never produce outputs for the sake of filling the void.
3. Output must be legible, readable, meaningfully organized, neat, clean, compact, precise, and concise.
4. Avoid generic terms, such as data and model. Instead, be specific and use observations, features, linear regression, logistic regression, etc.
5. No overplotting. No redundant plots. No data dumps (pages of table outputs).
6. Annotate your programming code! Help your audience to understand your abstraction.
7. All code must execute in Google Colab and Python 3.x without error/warning messages or local file dependencies.
   
8. Always start your modeling with a simple baseline model (version 0), which you can do with minimal data munging.
   1. This is your Proof of Concept and benchmark for any future improvement.
9. Start your work early! Never procrastinate. We are busy on assignment due days and may be inaccessible to answer last minute questions.
10. Clearly label problems and order them as assigned (to help us avoid missing your solutions)
11. IF late submissions are allowed, there will be 10% penalty per day. (Start early, manage your time, expect to get stuck!)

Course Policies

RE: Illness: Instructors can extend any assignments, such as Labs and essays. All of these are untimed. Please notify instructors of your situation, the affected assignments and the proposed deadline. If suitable, please attach completed submissions to have it timestamped.
 
Just in case, please familiarize yourselves with the JHU's policy on absences due to illness.

Academic Policies

Deadlines for Adding, Dropping, and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar. Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course. 

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students. This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Our courses are designed with a proactive approach to accessibility to minimize the need for disability disclosure and accommodation requests, but we recognize that you may need additional support. Students with disabilities (including those with psychological conditions, medical conditions, and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.  For further information or to start the process of requesting accommodations, please contact EP Student Disability Services at ep-disability-svcs@jhu.edu. 

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. For a full description of the code please visit the Student Conduct Code website.

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team (EP-Registration@exchange.johnshopkins.edu) in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.