IAM

TAG»DEEP LEARNING«

SEPTEMBER2023

PROJECT

Achieving accuracy, fair and private image classification.

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SEPTEMBER2023

PROJECT

Keeping track of generated images using watermarking.

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ARTICLE

Proper Robustness Evaluation of Confidence-Calibrated Adversarial Training in PyTorch

Properly evaluating defenses against adversarial examples has been difficult as adversarial attacks need to be adapted to each individual defense. This also holds for confidence-calibrated adversarial training, where robustness is obtained by rejecting adversarial examples based on their confidence. Thus, regular robustness metrics and attacks are not easily applicable. In this article, I want to discuss how to evaluate confidence-calibrated adversarial training in terms of metrics and attacks.

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ARTICLE

Generalizing Adversarial Robustness with Confidence-Calibrated Adversarial Training in PyTorch

Taking adversarial training from this previous article as baseline, this article introduces a new, confidence-calibrated variant of adversarial training that addresses two significant flaws: First, trained with L adversarial examples, adversarial training is not robust against L2 ones. Second, it incurs a significant increase in (clean) test error. Confidence-calibrated adversarial training addresses these problems by encouraging lower confidence on adversarial examples and subsequently rejecting them.

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JUNE2023

PROJECT

OPEN SOURCE Bit Error Robustness in PyTorch Article Series I was planning to have an article series on bit error robustness in deep learning — similar to my article series on adversarial robustness — with accompanying PyTorch code. However, the recent progress in machine learning made me focus on other projects. Nevertheless, the articles should […]

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JUNE2023

PROJECT

Series of articles discussing adversarial robustness and adversarial training in PyTorch.

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ARTICLE

47.9% Robust Test Error on CIFAR10 with Adversarial Training and PyTorch

Knowing how to compute adversarial examples from this previous article, it would be ideal to train models for which such adversarial examples do not exist. This is the goal of developing adversarially robust training procedures. In this article, I want to describe a particularly popular approach called adversarial training. The idea is to train on adversarial examples computed during training on-the-fly. I will also discuss a PyTorch implementation that obtains 47.9% robust test error — 52.1% robust accuracy — on CIFAR10 using a WRN-28-10 architecture.

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ARTICLE

Some Research Ideas for Conformal Training

With our paper on conformal training, we showed how conformal prediction can be integrated into end-to-end training pipelines. There are so many interesting directions of how to improve and build upon conformal training. Unfortunately, I just do not have the bandwidth to pursue all of them. So, in this article, I want to share some research ideas so others can pick them up.

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ARTICLE

Lp Adversarial Examples using Projected Gradient Descent in PyTorch

Adversarial examples, slightly perturbed images causing mis-classification, have received considerable attention over the last few years. While many different adversarial attacks have been proposed, projected gradient descent (PGD) and its variants is widely spread for reliable evaluation or adversarial training. In this article, I want to present my implementation of PGD to generate L, L2, L1 and L0 adversarial examples. Besides using several iterations and multiple attempts, the worst-case adversarial example across all iterations is returned and momentum as well as backtracking strengthen the attack.

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