Efficiently generating sentence-level textual adversarial examples with Seq2seq Stacked Auto-Encoder

Li, Ang; Zhang, Fangyuan; Li, Shuangjiao; Chen, Tianhua; S, Pan; Wang, Hongtao

doi:10.1016/j.eswa.2022.119170

Cited by 6 publications

(2 citation statements)

References 78 publications

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“…(2) Physical attacks 16 – 19 generate adversarial perturbations on physically existing objects. The most prominent existing physical attack, patch attacks, 16 , 20 are pasting adversarial patches or disguises onto the target object for the attack. Another type of physical attack 17 , 19 is projecting or emitting light or laser onto the target object for the attack.…”

Section: Introductionmentioning

confidence: 99%

Deceptive deraining attack: a restorative adversarial attack by rain removal

Liu,

Shao,

Meng

et al. 2024

J. Electron. Imag.

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Restoring images captured in adverse weather conditions can significantly enhance their visual quality, with widespread applications in fields such as autonomous driving. However, we observed a potential threat after image restoration: while the restored images are visually better, they may be adversarial for deep neural networks (DNNs). In light of this discovery, in this work, we explore a restorative attack approach, aiming to introduce adversarial perturbations in insensitive regions of the image while restoring images. In particular, we propose the deceptive deraining attack (DDA) method to generate more natural adversarial examples in the deraining scenario, denoted as DDA. In contrast to existing adversarial attack methods that degrade image quality, our DDA can efficiently introduce adversarial perturbations while enhancing the visual quality of rainy images. Specifically, we first restore the rainy image and derive the rain streak mask. Then the adversarial perturbations are added to the rain streak areas of the image according to the mask, and the perturbations are optimized by maximizing the classification loss to generate the adversarial image. Extensive experiments on synthetic rain datasets and real rain datasets indicate that the adversarial examples generated by our DAA are visually natural and maintain a high attack success rate on state-of-the-art DNNs.

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Section: Introductionmentioning

confidence: 99%

Deceptive deraining attack: a restorative adversarial attack by rain removal

Liu,

Shao,

Meng

et al. 2024

J. Electron. Imag.

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“…Auto encoder [18], which was introduced by Hinton and Salakhutdinov in 2006, is a neural network that can be stacked to form a deep architecture to perform nonlinear dimensionality reduction in raw data. Nowadays, it has been applied successfully in many areas, such as images [19], text [20], and audio [21]. An automatic encoder can be used to learn more useful and complex representations in neural networks with local denoising criteria [22,23].…”

Section: Introductionmentioning

confidence: 99%

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

Wang,

Zheng,

Wang

et al. 2024

Systems

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With the continuous development of information technology and the rapid increase in new users of social networking sites, recommendation technology is becoming more and more important. After research, it was found that the behavior of users on social networking sites has a great correlation with their personalities. The five characteristics of the OCEAN personality model can cover all aspects of a user’s personality. In this research, a micro-directional propagation model based on the OCEAN personality model and a Stacked Denoising Auto Encoder (SDAE) was built through the application of deep learning to a collaborative filtering technique. Firstly, the dimension of the user and item feature matrices was lowered using SDAE in order to extract deeper information. The user OCEAN personality model matrix and the reduced user feature matrix were integrated to create a new user feature matrix. Finally, the multiple linear regression approach was used to predict user-unrated goods and generate recommendations. This approach allowed us to leverage the relationships between various factors to deliver personalized recommendations. This experiment evaluated the RMSE and MAE of the model. The evaluation results show that the stacked denoising auto encoder collaborative filtering algorithm can improve the accuracy of recommendations, and the user’s OCEAN personality model improves the accuracy of the model to a certain extent.

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Non-Alpha-Num: a novel architecture for generating adversarial examples for bypassing NLP-based clickbait detection mechanisms

Bajaj,

Vishwakarma

2024

Int. J. Inf. Secur.

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Efficiently generating sentence-level textual adversarial examples with Seq2seq Stacked Auto-Encoder

Cited by 6 publications

References 78 publications

Deceptive deraining attack: a restorative adversarial attack by rain removal

Deceptive deraining attack: a restorative adversarial attack by rain removal

Stacked Noise Reduction Auto Encoder–OCEAN: A Novel Personalized Recommendation Model Enhanced

Non-Alpha-Num: a novel architecture for generating adversarial examples for bypassing NLP-based clickbait detection mechanisms

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