MBA: Multimodal Bidirectional Attack for Referring Expression Segmentation Models

• URL: http://arxiv.org/abs/2506.16157v1
• Date: Thu, 19 Jun 2025 09:14:04 GMT
• Title: MBA: Multimodal Bidirectional Attack for Referring Expression Segmentation Models
• Authors: Xingbai Chen, Tingchao Fu, Renyang Liu, Wei Zhou, Chao Yi,
• Abstract summary: Referring Expression (RES) enables precise object segmentation in images based on natural language descriptions.<n>Despite its impressive performance, the robustness of RES models against adversarial examples remains largely unexplored.<n>We propose a novel adversarial attack strategy termed textbfMultimodal Bidirectional Attack, tailored for RES models.
• Score: 2.5931446496646204
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Expression Segmentation (RES) enables precise object segmentation in images based on natural language descriptions, offering high flexibility and broad applicability in real-world vision tasks. Despite its impressive performance, the robustness of RES models against adversarial examples remains largely unexplored. While prior adversarial attack methods have explored adversarial robustness on conventional segmentation models, they perform poorly when directly applied to RES, failing to expose vulnerabilities in its multimodal structure. Moreover, in practical open-world scenarios, users typically issue multiple, diverse referring expressions to interact with the same image, highlighting the need for adversarial examples that generalize across varied textual inputs. To address these multimodal challenges, we propose a novel adversarial attack strategy termed \textbf{Multimodal Bidirectional Attack}, tailored for RES models. Our method introduces learnable proxy textual embedding perturbation and jointly performs visual-aligned optimization on the image modality and textual-adversarial optimization on the textual modality during attack generation. This dual optimization framework encourages adversarial images to actively adapt to more challenging text embedding during optimization, thereby enhancing their cross-text transferability, which refers to the ability of adversarial examples to remain effective under a variety of unseen or semantically diverse textual inputs. Extensive experiments conducted on multiple RES models and benchmark datasets demonstrate the superior effectiveness of our method compared to existing methods.

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