Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: REFIT: A Unified Watermark Removal Framework For Deep Learning Systems
  With Limited Data   Training deep neural networks from scratch could be computationally expensive
and requires a lot of training data. Recent work has explored different
watermarking techniques to protect the pre-trained deep neural networks from
potential copyright infringements. However, these techniques could be
vulnerable to watermark removal attacks. In this work, we propose REFIT, a
unified watermark removal framework based on fine-tuning, which does not rely
on the knowledge of the watermarks, and is effective against a wide range of
watermarking schemes. In particular, we conduct a comprehensive study of a
realistic attack scenario where the adversary has limited training data, which
has not been emphasized in prior work on attacks against watermarking schemes.
To effectively remove the watermarks without compromising the model
functionality under this weak threat model, we propose two techniques that are
incorporated into our fine-tuning framework: (1) an adaption of the elastic
weight consolidation (EWC) algorithm, which is originally proposed for
mitigating the catastrophic forgetting phenomenon; and (2) unlabeled data
augmentation (AU), where we leverage auxiliary unlabeled data from other
sources. Our extensive evaluation shows the effectiveness of REFIT against
diverse watermark embedding schemes. In particular, both EWC and AU
significantly decrease the amount of labeled training data needed for effective
watermark removal, and the unlabeled data samples used for AU do not
necessarily need to be drawn from the same distribution as the benign data for
model evaluation. The experimental results demonstrate that our fine-tuning
based watermark removal attacks could pose real threats to the copyright of
pre-trained models, and thus highlight the importance of further investigating
the watermarking problem and proposing more robust watermark embedding schemes
against the attacks.

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The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
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The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
3
The categories of this paper are: physics
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
2
The categories of this paper are: math
0
The categories of this paper are: hep-ph
4
The categories of this paper are: cond-mat