Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation

• URL: http://arxiv.org/abs/2406.19413v1
• Date: Tue, 18 Jun 2024 14:07:27 GMT
• Title: Saliency Attention and Semantic Similarity-Driven Adversarial Perturbation
• Authors: Hetvi Waghela, Jaydip Sen, Sneha Rakshit,
• Abstract summary: Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP) is designed to improve the effectiveness of contextual perturbations. Our proposed approach incorporates a three-pronged strategy for word selection and perturbation. SASSP has yielded a higher attack success rate and lower word perturbation rate.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we introduce an enhanced textual adversarial attack method, known as Saliency Attention and Semantic Similarity driven adversarial Perturbation (SASSP). The proposed scheme is designed to improve the effectiveness of contextual perturbations by integrating saliency, attention, and semantic similarity. Traditional adversarial attack methods often struggle to maintain semantic consistency and coherence while effectively deceiving target models. Our proposed approach addresses these challenges by incorporating a three-pronged strategy for word selection and perturbation. First, we utilize a saliency-based word selection to prioritize words for modification based on their importance to the model's prediction. Second, attention mechanisms are employed to focus perturbations on contextually significant words, enhancing the attack's efficacy. Finally, an advanced semantic similarity-checking method is employed that includes embedding-based similarity and paraphrase detection. By leveraging models like Sentence-BERT for embedding similarity and fine-tuned paraphrase detection models from the Sentence Transformers library, the scheme ensures that the perturbed text remains contextually appropriate and semantically consistent with the original. Empirical evaluations demonstrate that SASSP generates adversarial examples that not only maintain high semantic fidelity but also effectively deceive state-of-the-art natural language processing models. Moreover, in comparison to the original scheme of contextual perturbation CLARE, SASSP has yielded a higher attack success rate and lower word perturbation rate.

Related papers

• Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum [13.305800254250789]
  We propose a novel framework, Stable Diffusion-based Momentum Integrated Adversarial Examples (SD-MIAE) It generates adversarial examples that can effectively mislead neural network classifiers while maintaining visual imperceptibility and preserving the semantic similarity to the original class label. Experimental results demonstrate that SD-MIAE achieves a high misclassification rate of 79%, improving by 35% over the state-of-the-art method.
  arXiv  Detail & Related papers  (2024-10-17T01:22:11Z)
• COT: A Generative Approach for Hate Speech Counter-Narratives via Contrastive Optimal Transport [25.73474734479759]
  This research paper introduces a novel framework based on contrastive optimal transport. It effectively addresses the challenges of maintaining target interaction and promoting diversification in generating counter-narratives. Our proposed model significantly outperforms current methods evaluated by metrics from multiple aspects.
  arXiv  Detail & Related papers  (2024-06-18T06:24:26Z)
• A Modified Word Saliency-Based Adversarial Attack on Text Classification Models [0.0]
  This paper introduces a novel adversarial attack method targeting text classification models. The Modified Word Saliency-based Adversarial At-tack (MWSAA) aims to mislead classification models while preserving semantic coherence. Empirical evaluations conducted on diverse text classification datasets demonstrate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2024-03-17T18:39:14Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• Semantic Image Attack for Visual Model Diagnosis [80.36063332820568]
  In practice, metric analysis on a specific train and test dataset does not guarantee reliable or fair ML models. This paper proposes Semantic Image Attack (SIA), a method based on the adversarial attack that provides semantic adversarial images.
  arXiv  Detail & Related papers  (2023-03-23T03:13:04Z)
• In and Out-of-Domain Text Adversarial Robustness via Label Smoothing [64.66809713499576]
  We study the adversarial robustness provided by various label smoothing strategies in foundational models for diverse NLP tasks. Our experiments show that label smoothing significantly improves adversarial robustness in pre-trained models like BERT, against various popular attacks. We also analyze the relationship between prediction confidence and robustness, showing that label smoothing reduces over-confident errors on adversarial examples.
  arXiv  Detail & Related papers  (2022-12-20T14:06:50Z)
• Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation [59.01297461453444]
  We propose a hierarchical contrastive learning mechanism, which can unify hybrid granularities semantic meaning in the input text. Experiments demonstrate that our model outperforms competitive baselines on paraphrasing, dialogue generation, and storytelling tasks.
  arXiv  Detail & Related papers  (2022-05-26T13:26:03Z)
• Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation [21.594361495948316]
  We propose a novel feature-level adversarial training method named FLAT. FLAT incorporates variational word masks in neural networks to learn global word importance. Experiments show the effectiveness of FLAT in improving the robustness with respect to both predictions and interpretations.
  arXiv  Detail & Related papers  (2022-03-23T20:04:14Z)
• Learning-based Hybrid Local Search for the Hard-label Textual Attack [53.92227690452377]
  We consider a rarely investigated but more rigorous setting, namely hard-label attack, in which the attacker could only access the prediction label. Based on this observation, we propose a novel hard-label attack, called Learning-based Hybrid Local Search (LHLS) algorithm. Our LHLS significantly outperforms existing hard-label attacks regarding the attack performance as well as adversary quality.
  arXiv  Detail & Related papers  (2022-01-20T14:16:07Z)
• Towards Robust Speech-to-Text Adversarial Attack [78.5097679815944]
  This paper introduces a novel adversarial algorithm for attacking the state-of-the-art speech-to-text systems, namely DeepSpeech, Kaldi, and Lingvo. Our approach is based on developing an extension for the conventional distortion condition of the adversarial optimization formulation. Minimizing over this metric, which measures the discrepancies between original and adversarial samples' distributions, contributes to crafting signals very close to the subspace of legitimate speech recordings.
  arXiv  Detail & Related papers  (2021-03-15T01:51:41Z)
• Generating Natural Language Attacks in a Hard Label Black Box Setting [3.52359746858894]
  We study an important and challenging task of attacking natural language processing models in a hard label black box setting. We propose a decision-based attack strategy that crafts high quality adversarial examples on text classification and entailment tasks.
  arXiv  Detail & Related papers  (2020-12-29T22:01:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.