Multi-granular Adversarial Attacks against Black-box Neural Ranking Models
arxiv(2024)
摘要
Adversarial ranking attacks have gained increasing attention due to their
success in probing vulnerabilities, and, hence, enhancing the robustness, of
neural ranking models. Conventional attack methods employ perturbations at a
single granularity, e.g., word-level or sentence-level, to a target document.
However, limiting perturbations to a single level of granularity may reduce the
flexibility of creating adversarial examples, thereby diminishing the potential
threat of the attack. Therefore, we focus on generating high-quality
adversarial examples by incorporating multi-granular perturbations. Achieving
this objective involves tackling a combinatorial explosion problem, which
requires identifying an optimal combination of perturbations across all
possible levels of granularity, positions, and textual pieces. To address this
challenge, we transform the multi-granular adversarial attack into a sequential
decision-making process, where perturbations in the next attack step are
influenced by the perturbed document in the current attack step. Since the
attack process can only access the final state without direct intermediate
signals, we use reinforcement learning to perform multi-granular attacks.
During the reinforcement learning process, two agents work cooperatively to
identify multi-granular vulnerabilities as attack targets and organize
perturbation candidates into a final perturbation sequence. Experimental
results show that our attack method surpasses prevailing baselines in both
attack effectiveness and imperceptibility.
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