CS-E4001 - Research Seminar in Computer Science D: Research Seminar on Security and Privacy of Machine Learning, 02.03.2021-28.05.2021

Leading discussion session

• Leading discussion session
  

  Leading discussion session

  Guidelines

  Leading a discussion on a paper is composed of 2 parts taking 50 minutes altogether.

  
  

  1. A presentation type power point composed of the following items (20 minutes):
  

  1.a. An objective paper presentation that contains for instance:

  • Problem statement
  • Adversary/threat model
  • Summary of main findings & contributions
  • Results

  1.b. A critical personal synthesis that contains for instance:

  • Analysis of correctness/completeness
  • Potential flaws
  • Relation to related work
  • (A support for following discussion)
  • Etc.

  
  

  2. An interactive discussion with the rest of the class (30 minutes)

  • Prepare a set of points to discuss
  • Make it interactive and raise issues where opinions are likely to be divided
  • Develop provocative opinions
  • Ask controversial questions
  • Correlate research with recent events (e.g., news headlines on the use of AI)

  
  

  Paper assignment

  Go to this Google form and select 5 papers that you would like to present before Monday March 08, 23:55
  

  
  

  Presentation assignment:

  Discussion session	Title	Presenter
  1. Model evasion	Devil’s Whisper: A General Approach for Physical...	Albert Mohwald
  	On Adaptive Attacks to Adversarial Example Defenses	Oliver Jarnefelt
  2. Model poisoning	Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization	Seb
  	Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning	Yujia Guo
  3. Compromised training	BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain	Paavo Reinikka
  	Machine Learning Models that Remember Too Much	Ananth Mahadevan
  4. Model stealing	High Accuracy and High Fidelity Extraction of Neural Networks	Albert Mohwald
  	Imitation Attacks and Defenses for Black-box Machine Translation Systems	Yujia Guo
  5. Protecting intellectual property of models	Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring	Buse
  	DAWN: Dynamic Adversarial Watermarking of Neural Networks	Samuel
  6. Training data leakage	The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks	Samuel
  	ML-Leaks: Model and Data Independent Membership Inference Attacks...
  6. Tracing training data	Radioactive data: tracing through training	Oliver Jarnefelt
  	Auditing Data Provenance in Text-Generation Models
  7. Privacy-preserving training	Learning Differentially Private Recurrent Language Models
  	Auditing Differentially Private Machine Learning: How Private is Private SGD?	Paavo Reinikka
  7. Fairness & bias in ML prediction	Characterising Bias in Compressed Models	Ananth Mahadevan
  	On the Privacy Risks of Algorithmic Fairness

  
  

  
  

  
  

  • AssignmentPresentation 1: Slides Assignment

    Make a submission
  • AssignmentPresentation 2: Slides Assignment

    Make a submission