Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks

• URL: http://arxiv.org/abs/2605.06238v1
• Date: Thu, 07 May 2026 13:24:40 GMT
• Title: Band Together: Untargeted Adversarial Training with Multimodal Coordination against Evasion-based Promotion Attacks
• Abstract summary: Multimodal recommender systems are vulnerable to evasion-based promotion attacks.<n>We propose Untargeted Adversarial Training with Multimodal Coordination (UAT-MC) to address this issue.
• Score: 43.692750193227674
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multimodal recommender systems exploit visual and textual signals to alleviate data sparsity, but this also makes them more vulnerable to evasion-based promotion attacks. Existing defenses are largely limited to single-modal settings and mainly focus on poisoning-based threats, leaving evasion-based threats underexplored. In this work, we first identify a cross-modal gradient mismatch under the multi-user promotion setting, where visual and textual perturbations are optimized in inconsistent directions due to the dominance of distinct user groups. This phenomenon dilutes the attack effectiveness and leads robust training to underestimate worst-case risks. To address this issue, we propose Untargeted Adversarial Training with Multimodal Coordination (UAT-MC). UAT-MC tackles the challenge of unknown targeted items in evasion-based attacks (as opposed to poisoning-based attacks) by treating all items as potential targets, and introduces a gradient alignment mechanism to explicitly correct this mismatch. This design ensures synchronized perturbations across modalities, thereby maximizing adversarial strength for robust training. Extensive experiments demonstrate that UAT-MC significantly improves robustness against promotion attacks while maintaining acceptable recommendation performance under the defense-accuracy trade-off. Code is available at https://github.com/gmXian/UAT-MC.

Related papers

• Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems [51.504307822017985]
  Large language model-powered sequential recommender systems (LLM-SRSs) have recently demonstrated remarkable performance.<n>This paper investigates the item promotion attack in LLM-SRSs under a more realistic setting where both the system prompt and victim model are unknown to the attacker.
  arXiv  Detail & Related papers  (2026-04-26T10:09:26Z)
• Diversifying Counterattacks: Orthogonal Exploration for Robust CLIP Inference [45.723695657400576]
  We argue that enhancing the diversity and coverage of counterattacks is crucial to improving adversarial robustness in test-time defense.<n>We propose Directional Orthogonal Counterattack (DOC), which augments counterattack optimization by incorporating gradient directions and momentum-based updates.<n>We present a directional sensitivity score based on averaged cosine similarity to boost DOC by improving example discrimination and adaptively modulating the counterattack strength.
  arXiv  Detail & Related papers  (2025-11-12T07:40:16Z)
• DOPA: Stealthy and Generalizable Backdoor Attacks from a Single Client under Challenging Federated Constraints [2.139012072214621]
  Federated Learning (FL) is increasingly adopted for privacy-preserving collaborative training, but its decentralized nature makes it susceptible to backdoor attacks.<n>Existing attack methods, however, often rely on idealized assumptions and fail to remain effective under real-world constraints.<n>We propose DOPA, a novel framework that simulates heterogeneous local training dynamics and seeks consensus across divergent optimization trajectories to craft universally effective and stealthy backdoor triggers.
  arXiv  Detail & Related papers  (2025-08-20T08:39:12Z)
• Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks [62.036798488144306]
  Current defense mainly focuses on the known attacks, but the adversarial robustness to the unknown attacks is seriously overlooked. We propose an attack-agnostic defense method named Meta Invariance Defense (MID) We show that MID simultaneously achieves robustness to the imperceptible adversarial perturbations in high-level image classification and attack-suppression in low-level robust image regeneration.
  arXiv  Detail & Related papers  (2024-04-04T10:10:38Z)
• Multi-granular Adversarial Attacks against Black-box Neural Ranking Models [111.58315434849047]
  We create high-quality adversarial examples by incorporating multi-granular perturbations. We transform the multi-granular attack into a sequential decision-making process. Our attack method surpasses prevailing baselines in both attack effectiveness and imperceptibility.
  arXiv  Detail & Related papers  (2024-04-02T02:08:29Z)
• 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)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• Saliency Diversified Deep Ensemble for Robustness to Adversaries [1.9659095632676094]
  This work proposes a novel diversity-promoting learning approach for the deep ensembles. The idea is to promote saliency map diversity (SMD) on ensemble members to prevent the attacker from targeting all ensemble members at once. We empirically show a reduced transferability between ensemble members and improved performance compared to the state-of-the-art ensemble defense.
  arXiv  Detail & Related papers  (2021-12-07T10:18:43Z)

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.