SoK: Pitfalls in Evaluating Black-Box Attacks
Post by Anshuman Suri and Fnu Suya Much research has studied black-box attacks on image classifiers, where adversaries generate adversarial examples against unknown target models without having access to their internal information. Our analysis of over 164 attacks (published in 102 major security, machine learning and security conferences) shows how these works make different assumptions about the adversary’s knowledge. The current literature lacks cohesive organization centered around the threat model. Our SoK paper (to appear at IEEE SaTML 2024) introduces a taxonomy for systematizing these attacks and demonstrates the importance of careful evaluations that consider adversary resources and threat models.NeurIPS 2023: What Distributions are Robust to Poisoning Attacks?
Post by Fnu Suya Data poisoning attacks are recognized as a top concern in the industry [1]. We focus on conventional indiscriminate data poisoning attacks, where an adversary injects a few crafted examples into the training data with the goal of increasing the test error of the induced model. Despite recent advances, indiscriminate poisoning attacks on large neural networks remain challenging [2]. In this work (to be presented at NeurIPS 2023), we revisit the vulnerabilities of more extensively studied linear models under indiscriminate poisoning attacks.Adjectives Can Reveal Gender Biases Within NLP Models
Post by Jason Briegel and Hannah Chen Because NLP models are trained with human corpora (and now, increasingly on text generated by other NLP models that were originally trained on human language), they are prone to inheriting common human stereotypes and biases. This is problematic, because with their growing prominence they may further propagate these stereotypes (Sun et al., 2019). For example, interest is growing in mitigating bias in the field of machine translation, where systems such as Google translate were observed to default to translating gender-neutral pronouns as male pronouns, even with feminine cues (Savoldi et al.Congratulations, Dr. Suya!
Congratulations to Fnu Suya for successfully defending his PhD thesis! Suya will join the Unversity of Maryland as a MC2 Postdoctoral Fellow at the Maryland Cybersecurity Center this fall. On the Limits of Data Poisoning Attacks Current machine learning models require large amounts of labeled training data, which are often collected from untrusted sources. Models trained on these potentially manipulated data points are prone to data poisoning attacks. My research aims to gain a deeper understanding on the limits of two types of data poisoning attacks: indiscriminate poisoning attacks, where the attacker aims to increase the test error on the entire dataset; and subpopulation poisoning attacks, where the attacker aims to increase the test error on a defined subset of the distribution.SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning
Our paper on the use of cryptographic-style games to model inference privacy is published in IEEE Symposium on Security and Privacy (Oakland):
Giovanni Cherubin, , Boris Köpf, Andrew Paverd, Anshuman Suri, Shruti Tople, and Santiago Zanella-Béguelin. SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning. IEEE Symposium on Security and Privacy, 2023. [Arxiv]
Tired of diverse definitions of machine learning privacy risks? Curious about game-based definitions? In our paper, we present privacy games as a tool for describing and analyzing privacy risks in machine learning. Join us on May 22nd, 11 AM @IEEESSP '23 https://t.co/NbRuTmHyd2 pic.twitter.com/CIzsT7UY4b
— ahmed salem (@AhmedGaSalem) May 15, 2023
CVPR 2023: Manipulating Transfer Learning for Property Inference
Manipulating Transfer Learning for Property Inference Transfer learning is a popular method to train deep learning models efficiently. By reusing parameters from upstream pre-trained models, the downstream trainer can use fewer computing resources to train downstream models, compared to training models from scratch. The figure below shows the typical process of transfer learning for vision tasks: However, the nature of transfer learning can be exploited by a malicious upstream trainer, leading to severe risks to the downstream trainer.MICO Challenge in Membership Inference
Anshuman Suri wrote up an interesting post on his experience with the MICO Challenge, a membership inference competition that was part of SaTML. Anshuman placed second in the competition (on the CIFAR data set), where the metric is highest true positive rate at a 0.1 false positive rate over a set of models (some trained using differential privacy and some without). Anshuman’s post describes the methods he used and his experience in the competition: My submission to the MICO Challenge.Voice of America interview on ChatGPT
I was interviewed for a Voice of America story (in Russian) on the impact of chatGPT and similar tools.
Full story: https://youtu.be/dFuunAFX9y4