| SPECIAL SECTION: BIG DATA |
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Attack-guided diffusion model for Chinese adversarial samples generation |
| WU Houyue1, LI Xianwei1,2, ZHANG Shunxiang3,4, ZHU Honghao1, WANG Ting5 |
1. School of Computer and Information Engineering, Bengbu University, Bengbu 233030, China;
2. Anhui Engineering Research Center for Intelligent Applications and Security of Industrial Internet, Anhui University of Technology, Ma'anshan 243032, China;
3. School of Computer Science and Engineering, Anhui University of Science & Technology, Huainan 232000, China;
4. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 240088, China;
5. School of Information Engineering, Huainan Union University, Huainan 232000, China |
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Abstract [Objective] The generation of adversarial samples in text represents a significant area of research in natural language processing. The process is employed to test the robustness of machine learning models and has gained widespread attention from scholars. Owing to the complex nature of Chinese semantics, generating Chinese adversarial samples remains a major challenge. Traditional methods for generating Chinese adversarial samples mainly involve word replacement, deletion/insertion, and word order adjustment. These methods often produce samples that are easily detectable and have low attack success rates, and thus, the methods struggle to balance attack effectiveness and semantic coherence. To address these limitations, this study introduces DiffuAdv, a novel method for generating Chinese adversarial samples. This approach enhances the generation process by simulating the data distribution during the adversarial attack phase. The gradient changes between adversarial and original samples are used as guiding conditions during the model's reverse diffusion phase in pre-training, resulting in the generation of more natural and effective adversarial samples. [Methods] DiffuAdv entails the introduction of diffusion models into the generation of adversarial samples to improve attack success rates while ensuring the naturalness of the generated text. This method utilizes a gradient-guided diffusion process, leveraging gradient information between original and adversarial samples as guiding conditions. It consists of two stages: forward diffusion and reverse diffusion. In the forward diffusion stage, noise is progressively added to the original data until a noise-dominated state is achieved. The reverse diffusion stage involves the reconstruction of samples, in which the gradient changes between adversarial and original samples are leveraged to maximize the adversarial objective. During the pre-training phase, data capture and feature learning occur under gradient guidance, with the aim of learning the data distribution of original samples and analyzing the deviations from adversarial samples. In the reverse diffusion generation phase, adversarial perturbations are constructed using gradients and integrated into the reverse diffusion process, ensuring that at each step of reverse diffusion, samples evolve toward greater adversarial effectiveness. To validate the effectiveness of the proposed method, extensive experiments are conducted across multiple datasets and various natural language processing tasks, and the performance of the method is compared with those of seven existing state-of-the-art methods. [Results] Compared with existing methods for generating Chinese adversarial samples, DiffuAdv demonstrates higher attack success rates across three tasks: text sentiment classification, causal relation extraction, and sentiment cause extraction. Ablation experiments confirm the effectiveness of using gradient changes between original and adversarial samples to guide the generation of adversarial samples and improve their quality. Perplexity (PPL) measurements indicate that the adversarial samples generated by DiffuAdv have an average PPL value of only 0.518, demonstrating that these samples are superior in rationality and readability compared with the samples generated by other methods. [Conclusions] DiffuAdv effectively generates high-quality adversarial samples that closely resemble real text in terms of fluency and naturalness. The adversarial samples produced by this method not only achieve high attack success rates but also exhibit strong robustness. The introduction of DiffuAdv enhances the research perspective on generating adversarial text samples and broadens the approaches for tasks such as text sentiment classification, causal relationship extraction, and emotion-cause pair extraction.
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| Keywords
adversarial sample generation
guided diffusion
conditional diffusion
diffusion model
text generation
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Issue Date: 22 November 2024
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2024,
Vol. 64
