Adaptive‐capacity and robust natural language watermarking for agglutinative languages

Kim, Miyoung; Goebel, Randy

doi:10.1002/sec.336

Cited by 5 publications

(2 citation statements)

References 12 publications

(23 reference statements)

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“…Mainstream algorithms exploit the syntactic and semantic nature of texts for watermark embedding 43 – 46 They emphasize that the critical semantic information of the marked texts are consistent with the original ones. For example, secret bits can be embedded into a text by replacing specified words in the text with semantically similar words.…”

Section: Preliminariesmentioning

confidence: 99%

Generating watermarked adversarial texts

Wang

et al. 2023

J. Electron. Imag.

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Adversarial example generation (AEG) has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals the vulnerability of DNNs, motivating us to find good solutions to improve the robustness of DNN models. Due to the extensiveness and high liquidity of natural language over the social networks, various natural language-based adversarial attack algorithms have been proposed in the literature. These algorithms generate adversarial text examples with high semantic quality. However, the generated adversarial text examples and the corresponding attack models may be maliciously or illegally used. To tackle this problem, we present a general framework encapsulated in the cloud application programming interfaces (APIs) for generating watermarked adversarial text examples to protect adversarial text examples and corresponding adversarial text attack models. For each word in a given text, a set of candidate words are determined to ensure that all the words in the set can be used to carry secret bits or facilitate the construction of adversarial example. By applying a word-level adversarial text generation algorithm, the watermarked adversarial text example can be finally generated. Experiment results show that the adversarial text examples generated by the proposed method not only successfully fool advanced DNN models, but also carry watermarks that can effectively verify the ownership and trace the source of the adversarial examples and the corresponding attack models. Moreover, the watermark can still survive after attacked with AEG algorithms, which has shown the applicability and superiority.

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Section: Preliminariesmentioning

confidence: 99%

Generating watermarked adversarial texts

Wang

et al. 2023

J. Electron. Imag.

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“…However, these format based algorithms have limited robustness since the text format can be easily changed by an intentional attacker, resulting in a high bit error rate. Mainstream algorithms exploit the syntactic and semantic nature of texts for watermark embedding [42]- [45]. They emphasize that the critical semantic information of the marked texts are consistent with the original ones.…”

Section: B Text Watermarkingmentioning

confidence: 99%

Generating Watermarked Adversarial Texts

Li¹,

Wu²,

Zhang³

2021

Preprint

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Adversarial example generation has been a hot spot in recent years because it can cause deep neural networks (DNNs) to misclassify the generated adversarial examples, which reveals the vulnerability of DNNs, motivating us to find good solutions to improve the robustness of DNN models. Due to the extensiveness and high liquidity of natural language over the social networks, various natural language based adversarial attack algorithms have been proposed in the literature. These algorithms generate adversarial text examples with high semantic quality. However, the generated adversarial text examples may be maliciously or illegally used. In order to tackle with this problem, we present a general framework for generating watermarked adversarial text examples. For each word in a given text, a set of candidate words are determined to ensure that all the words in the set can be used to either carry secret bits or facilitate the construction of adversarial example. By applying a word-level adversarial text generation algorithm, the watermarked adversarial text example can be finally generated. Experiments show that the adversarial text examples generated by the proposed method not only successfully fool advanced DNN models, but also carry a watermark that can effectively verify the ownership and trace the source of the adversarial examples. Moreover, the watermark can still survive after attacked with adversarial example generation algorithms, which has shown the applicability and superiority.

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