Post by Katherine Knipmeyer
Machine learning poses a substantial risk that adversaries will be able to discover information that the model does not intend to reveal. One set of methods by which consumers can learn this sensitive information, known broadly as membership inference attacks, predicts whether or not a query record belongs to the training set. A basic membership inference attack involves an attacker with a given record and black-box access to a model who tries to determine whether said record was a member of the model’s training set.
Post by Sicheng Zhu
With the rapid development of deep learning and the explosive growth of unlabeled data, representation learning is becoming increasingly important. It has made impressive applications such as pre-trained language models (e.g., BERT and GPT-3).
Popular as it is, representation learning raises concerns about the robustness of learned representations under adversarial settings. For example, how can we compare the robustness to different representations, and how can we build representations that enable robust downstream classifiers?
The video of Xiao’s presentation for AISTATS 2020 is now available: Understanding the Intrinsic Robustness of Image Distributions using Conditional Generative Models
Starting with Gilmer et al. (2018), several works have demonstrated the inevitability of adversarial examples based on different assumptions about the underlying input probability space. It remains unclear, however, whether these results apply to natural image distributions. In this work, we assume the underlying data distribution is captured by some conditional generative model, and prove intrinsic robustness bounds for a general class of classifiers, which solves an open problem in Fawzi et al.
Hannah Chen presented her paper on Pointwise Paraphrase Appraisal is Potentially Problematic at the ACL 2020 Student Research Workshop:
The prevailing approach for training and evaluating paraphrase identification models is constructed as a binary classification problem: the model is given a pair of sentences, and is judged by how accurately it classifies pairs as either paraphrases or non-paraphrases. This pointwise-based evaluation method does not match well the objective of most real world applications, so the goal of our work is to understand how models which perform well under pointwise evaluation may fail in practice and find better methods for evaluating paraphrase identification models.
This blog was started in January 2008, a bit over eight years after I started as a professor at UVA and initiated the research group. It was named after Thomas Jefferson’s cipher wheel, which has long been (and remains) one of my favorite ways to introduce cryptography.
Figuring out how to honor our history, including Jefferson’s founding of the University, and appreciate his ideals and enormous contributions, while confronting the reality of Jefferson as a slave owner and abuser, will be a challenge and responsibility for people above my administrative rank.
I chaired the committee to select Test-of-Time Awards for the IEEE Symposium on Security and Privacy symposia from 1995-2006, which were presented at the Opening Section of the 41stIEEE Symposium on Security and Privacy.
New: Video Presentation
Finding Black-box Adversarial Examples with Limited Queries Black-box attacks generate adversarial examples (AEs) against deep neural networks with only API access to the victim model.
Existing black-box attacks can be grouped into two main categories:
Transfer Attacks use white-box attacks on local models to find candidate adversarial examples that transfer to the target model.
Optimization Attacks use queries to the target model and apply optimization techniques to search for adversarial examples.
Xiao Zhang will present our work (with Saeed Mahloujifar and Mohamood Mahmoody) as a spotlight at NeurIPS 2019, Vancouver, 10 December 2019.
Recent theoretical results, starting with Gilmer et al.‘s Adversarial Spheres (2018), show that if inputs are drawn from a concentrated metric probability space, then adversarial examples with small perturbation are inevitable.c The key insight from this line of research is that concentration of measure gives lower bound on adversarial risk for a large collection of classifiers (e.