375 research outputs found
Learning Transferable Adversarial Robust Representations via Multi-view Consistency
Despite the success on few-shot learning problems, most meta-learned models
only focus on achieving good performance on clean examples and thus easily
break down when given adversarially perturbed samples. While some recent works
have shown that a combination of adversarial learning and meta-learning could
enhance the robustness of a meta-learner against adversarial attacks, they fail
to achieve generalizable adversarial robustness to unseen domains and tasks,
which is the ultimate goal of meta-learning. To address this challenge, we
propose a novel meta-adversarial multi-view representation learning framework
with dual encoders. Specifically, we introduce the discrepancy across the two
differently augmented samples of the same data instance by first updating the
encoder parameters with them and further imposing a novel label-free
adversarial attack to maximize their discrepancy. Then, we maximize the
consistency across the views to learn transferable robust representations
across domains and tasks. Through experimental validation on multiple
benchmarks, we demonstrate the effectiveness of our framework on few-shot
learning tasks from unseen domains, achieving over 10\% robust accuracy
improvements against previous adversarial meta-learning baselines.Comment: *Equal contribution (Author ordering determined by coin flip).
NeurIPS SafetyML workshop 2022, Under revie
Inverting Adversarially Robust Networks for Image Synthesis
Recent research in adversarially robust classifiers suggests their
representations tend to be aligned with human perception, which makes them
attractive for image synthesis and restoration applications. Despite favorable
empirical results on a few downstream tasks, their advantages are limited to
slow and sensitive optimization-based techniques. Moreover, their use on
generative models remains unexplored. This work proposes the use of robust
representations as a perceptual primitive for feature inversion models, and
show its benefits with respect to standard non-robust image features. We
empirically show that adopting robust representations as an image prior
significantly improves the reconstruction accuracy of CNN-based feature
inversion models. Furthermore, it allows reconstructing images at multiple
scales out-of-the-box. Following these findings, we propose an
encoding-decoding network based on robust representations and show its
advantages for applications such as anomaly detection, style transfer and image
denoising
Robustness of Unsupervised Representation Learning without Labels
Unsupervised representation learning leverages large unlabeled datasets and
is competitive with supervised learning. But non-robust encoders may affect
downstream task robustness. Recently, robust representation encoders have
become of interest. Still, all prior work evaluates robustness using a
downstream classification task. Instead, we propose a family of unsupervised
robustness measures, which are model- and task-agnostic and label-free. We
benchmark state-of-the-art representation encoders and show that none dominates
the rest. We offer unsupervised extensions to the FGSM and PGD attacks. When
used in adversarial training, they improve most unsupervised robustness
measures, including certified robustness. We validate our results against a
linear probe and show that, for MOCOv2, adversarial training results in 3 times
higher certified accuracy, a 2-fold decrease in impersonation attack success
rate and considerable improvements in certified robustness
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