Open AccessPosted Content
Cross-modal Adversarial Reprogramming.
Paarth Neekhara,Shehzeen Hussain,Jinglong Du,Shlomo Dubnov,Farinaz Koushanfar,Julian McAuley +5 more
TLDR
In this article, an efficient adversarial program that maps a sequence of discrete tokens into an image which can be classified to the desired class by an image classification model is proposed, achieving competitive performance on a variety of text and sequence classification benchmarks without retraining the network.Abstract:
With the abundance of large-scale deep learning models, it has become possible to repurpose pre-trained networks for new tasks. Recent works on adversarial reprogramming have shown that it is possible to repurpose neural networks for alternate tasks without modifying the network architecture or parameters. However these works only consider original and target tasks within the same data domain. In this work, we broaden the scope of adversarial reprogramming beyond the data modality of the original task. We analyze the feasibility of adversarially repurposing image classification neural networks for Natural Language Processing (NLP) and other sequence classification tasks. We design an efficient adversarial program that maps a sequence of discrete tokens into an image which can be classified to the desired class by an image classification model. We demonstrate that by using highly efficient adversarial programs, we can reprogram image classifiers to achieve competitive performance on a variety of text and sequence classification benchmarks without retraining the network.read more
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Posted Content
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference
Yang Zheng,Xiaoyi Feng,Xia Zhaoqiang,Xiaoyue Jiang,Ambra Demontis,Maura Pintor,Battista Biggio,Fabio Roli +7 more
TL;DR: In this article, the authors developed a first-order linear model of adversarial reprogramming to show that its success inherently depends on the size of the average input gradient, which grows when input gradients are more aligned, and when inputs have higher dimensionality.
References
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