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advertorch v0.1: An Adversarial Robustness Toolbox based on PyTorch
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advertorch is a toolbox for adversarial robustness research. It contains various implementations for attacks, defenses and robust training methods. advertorch is built on PyTorch (Paszke et al., 2017), and leverages the advantages of the dynamic computational graph to provide concise and efficient reference implementations. The code is licensed under the LGPL license and is open sourced at https://github.com/BorealisAI/advertorch .
Forward citations
Cited by 3 Pith papers
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How Do Diffusion Models Improve Adversarial Robustness?
Diffusion models improve adversarial robustness mainly by compressing the input space, while the large gains reported earlier mostly come from evaluation randomness.
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On the Promise for Assurance of Differentiable Neurosymbolic Reasoning Paradigms
A systematic comparison finds that differentiable neurosymbolic systems offer better assurance mainly in arithmetic-like reasoning tasks, not across the board, and interpretable shortcuts can increase adversarial risk.
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Adversarial Attacks against Closed-Source MLLMs via Feature Optimal Alignment
FOA-Attack aligns global and clustered local features via optimal transport with dynamic ensemble weighting to create targeted adversarial images that transfer to closed-source multimodal LLMs.
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