Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Enhancing Backdoor Attacks with Multi-Level MMD Regularization   While Deep Neural Networks (DNNs) excel in many tasks, the huge training
resources they require become an obstacle for practitioners to develop their
own models. It has become common to collect data from the Internet or hire a
third party to train models. Unfortunately, recent studies have shown that
these operations provide a viable pathway for maliciously injecting hidden
backdoors into DNNs. Several defense methods have been developed to detect
malicious samples, with the common assumption that the latent representations
of benign and malicious samples extracted by the infected model exhibit
different distributions. However, a comprehensive study on the distributional
differences is missing. In this paper, we investigate such differences
thoroughly via answering three questions: 1) What are the characteristics of
the distributional differences? 2) How can they be effectively reduced? 3) What
impact does this reduction have on difference-based defense methods? First, the
distributional differences of multi-level representations on the regularly
trained backdoored models are verified to be significant by introducing Maximum
Mean Discrepancy (MMD), Energy Distance (ED), and Sliced Wasserstein Distance
(SWD) as the metrics. Then, ML-MMDR, a difference reduction method that adds
multi-level MMD regularization into the loss, is proposed, and its
effectiveness is testified on three typical difference-based defense methods.
Across all the experimental settings, the F1 scores of these methods drop from
90%-100% on the regularly trained backdoored models to 60%-70% on the models
trained with ML-MMDR. These results indicate that the proposed MMD
regularization can enhance the stealthiness of existing backdoor attack
methods. The prototype code of our method is now available at
https://github.com/xpf/Multi-Level-MMD-Regularization.

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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: atom-ph
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The categories of this paper are: solv-int
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The categories of this paper are: hep-ex
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The categories of this paper are: nlin
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The categories of this paper are: nucl-ex
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The categories of this paper are: physics
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The categories of this paper are: q-fin
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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The categories of this paper are: eess
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat