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Found 5 papers, 2 papers with code
A Certified Radius-Guided Attack Framework to Image Segmentation Models
We are the first, from the attacker perspective, to leverage the properties of certified radius and propose a certified radius guided attack framework against image segmentation models.
REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder as a Service
For the first question, we show that the cloud service only needs to provide two APIs, which we carefully design, to enable a client to certify the robustness of its downstream classifier with a minimal number of queries to the APIs.
Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning
In this work, we perform the first systematic, principled measurement study to understand whether and when a pre-trained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples
In this work, we propose MultiGuard, the first provably robust defense against adversarial examples to multi-label classification.
EncoderMI: Membership Inference against Pre-trained Encoders in Contrastive Learning
EncoderMI can be used 1) by a data owner to audit whether its (public) data was used to pre-train an image encoder without its authorization or 2) by an attacker to compromise privacy of the training data when it is private/sensitive.
