Topic outline

  • 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



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