Enhancing Adversarial Robustness of Neural Ranking Models through Multi-granular Perturbations

Keskeiset käsitteet

The authors propose a novel reinforcement learning-based framework, RL-MARA, to generate multi-granular adversarial examples that can effectively attack black-box neural ranking models. RL-MARA incorporates perturbations at multiple levels of granularity, including word, phrase, and sentence, to exploit the diverse vulnerability distribution within documents and enhance the attack effectiveness.

Tiivistelmä

The paper focuses on developing a multi-granular adversarial attack framework, RL-MARA, to uncover the vulnerabilities of black-box neural ranking models (NRMs). Key highlights:

Existing adversarial ranking attack methods are limited to a single level of perturbation granularity, which may not fully capture the diverse vulnerability patterns in documents. RL-MARA addresses this by incorporating perturbations at word, phrase, and sentence levels.

RL-MARA formulates the multi-granular attack as a sequential decision-making process, where a sub-agent identifies vulnerable positions at different granularities, and a meta-agent generates and organizes the perturbations. Reinforcement learning is used to navigate this complex search space.

Experiments on two benchmark datasets, MS MARCO and ClueWeb09, show that RL-MARA significantly outperforms existing single-granular attack baselines in terms of attack effectiveness and imperceptibility of the generated adversarial examples.

RL-MARA's performance is evaluated across different target NRMs, including BERT, PROP, and RankLLM. The results indicate that RankLLM, a model distilled from large language models, exhibits higher adversarial robustness compared to the other NRMs.

The authors also investigate the impact of the balance between attack effectiveness and naturalness in the reward function, demonstrating the flexibility of RL-MARA in generating adversarial examples with varying degrees of naturalness.

Tilastot

The attack success rate (ASR) of RL-MARA against RankLLM on MS MARCO Hard documents is 65.4% higher than the best baseline, IDEM.
The average boosted ranks (Boost) of RL-MARA against RankLLM on MS MARCO Hard documents is 34.5% higher than IDEM.
The boosted top-5 rate (T5R) of RL-MARA against RankLLM on MS MARCO Mixture documents is 34.8% higher than the best baseline.

Lainaukset

"Existing studies on adversarial attacks against NRMs are typically restricted to document perturbation strategies that operate at a single level of granularity, such as the word-level or sentence-level."
"Limiting perturbations to a single granularity may fail to adequately capture the nuanced and diverse vulnerability features, a limitation confirmed by our experimental results."

Tärkeimmät oivallukset

Multi-granular Adversarial Attacks against Black-box Neural Ranking Models

by Yu-An Liu,Ru... klo arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01574.pdf

Syvällisempiä Kysymyksiä

How can the multi-granular attack framework be extended to incorporate more advanced perturbation methods at each granularity level

To extend the multi-granular attack framework to incorporate more advanced perturbation methods at each granularity level, several strategies can be implemented:

Advanced Word-Level Perturbations: Instead of simple word substitution, more sophisticated techniques like synonym replacement based on contextual embeddings or word embeddings can be utilized. This can help in generating more natural and effective adversarial examples.

Enhanced Phrase-Level Perturbations: Rather than just substituting phrases, techniques like paraphrasing or rephrasing can be employed to introduce more diverse and impactful perturbations at the phrase level.

Complex Sentence-Level Perturbations: Instead of straightforward sentence substitution, more intricate methods like generating adversarial triggers specific to the target document or leveraging reinforcement learning for sentence rewriting can be explored.

Hybrid Perturbation Strategies: Combining different perturbation methods at each granularity level can lead to more potent attacks. For instance, integrating word-level substitutions with phrase-level paraphrasing can create adversarial examples with a higher impact.

Dynamic Perturbation Selection: Implementing a dynamic perturbation selection mechanism that adapts based on the document content and context can enhance the diversity and effectiveness of perturbations at each granularity level.

By incorporating these advanced perturbation methods, the multi-granular attack framework can become more sophisticated and capable of generating highly effective adversarial examples against neural ranking models.

What are the potential countermeasures that can be developed to enhance the robustness of neural ranking models against multi-granular adversarial attacks

Countermeasures to enhance the robustness of neural ranking models against multi-granular adversarial attacks include:

Adversarial Training: Incorporating adversarial training during the model training phase can help the neural ranking model learn to resist various levels of perturbations and improve its robustness against adversarial attacks.

Regularization Techniques: Applying regularization methods like dropout, weight decay, or adversarial training can help prevent overfitting to specific perturbations and enhance the model's generalization capabilities.

Ensemble Learning: Utilizing ensemble learning by combining multiple neural ranking models with diverse architectures or training strategies can improve the model's resilience against adversarial attacks.

Input Sanitization: Implementing input sanitization techniques to detect and filter out adversarial perturbations before they impact the model's decision-making process can help mitigate the effects of multi-granular attacks.

Robust Evaluation Metrics: Developing robust evaluation metrics that consider both attack effectiveness and naturalness of adversarial examples can provide a comprehensive assessment of the model's vulnerability to multi-granular attacks.

By implementing these countermeasures, neural ranking models can enhance their robustness and reliability in the face of multi-granular adversarial attacks.

How can the insights from this work on adversarial vulnerabilities be leveraged to improve the overall reliability and trustworthiness of information retrieval systems

The insights gained from this work on adversarial vulnerabilities in neural ranking models can be leveraged to improve the overall reliability and trustworthiness of information retrieval systems in the following ways:

Enhanced Model Training: By incorporating adversarial training and robust optimization techniques based on the identified vulnerabilities, neural ranking models can be trained to be more resilient to adversarial attacks.

Continuous Monitoring: Implementing a system for continuous monitoring and detection of adversarial attacks can help information retrieval systems identify and mitigate potential threats in real-time.

Adaptive Defense Mechanisms: Developing adaptive defense mechanisms that can dynamically adjust to emerging adversarial strategies based on multi-granular attacks can strengthen the security of neural ranking models.

Transparent Evaluation: Ensuring transparent evaluation practices that consider both attack effectiveness and naturalness of adversarial examples can provide a more accurate assessment of the model's vulnerability and reliability.

Collaborative Research: Encouraging collaboration and knowledge-sharing among researchers and practitioners in the field of adversarial attacks and information retrieval can lead to the development of more robust and trustworthy systems.

By implementing these strategies and leveraging the insights from adversarial vulnerabilities, information retrieval systems can enhance their overall reliability and trustworthiness in the face of evolving adversarial threats.