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首次观测 · 记录 #1CVF · CVPR 2026:cvf:69fc23ecd7d4eb60b0dcbe0fbab075d3CVF · CVPR 2026

CamPI: Physical Adversarial Examples through Camera Power Signal Injection

Ren, Yanze, Lv, Mingyuan, Jiang, Qinhong, Jiang, Yan, Yan, Chen, Ji, Xiaoyu, Xu, Wenyuan

原文摘要

Physical adversarial examples pose a concrete threat to real-world computer vision systems. Existing works mainly generate physical adversarial examples by affixing patches or projecting light onto targets, which are usually visible and can expose the malicious intention. In this work, we reveal a new attack surface that generates invisible adversarial samples by injecting signals into the camera's power supply. We analyze the mechanism of injecting structural stripe patterns into cameras and demonstrate the feasibility of controllable fine-grained injection with signal modulation. We develop a simulation model to emulate the physically injected perturbation, and propose end-to-end optimization methodologies in both white-box and black-box settings to generate the injection signal parameters. We perform a simulated evaluation across seven classification models and carry out physical signal injection experiments with optimized signals. The results show that physical adversarial examples generated through camera power signal injection can disrupt computer vision performance. Our work introduces a new methodology for physical adversarial examples, emphasizing the need for securing computer vision systems in the physical world.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ab5293ccf5ebeebccf2ab8c48aac178bCVF · CVPR 2026

Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning

Zhou, Hao, Wu, Tiru, Jiang, Yan, Zhou, Wanqi, Hu, Junxing, Han, Ai

原文摘要

Multi-modal multi-agent systems (MM-MAS) have gained increasing attention for their capacity to enable complex reasoning and coordination across diverse modalities. As these systems continue to expand in scale and functionality, investigating their potential vulnerabilities has become increasingly important.However, existing studies on adversarial attacks in multi-agent systems primarily focus on isolated agents or unimodal settings, leaving the vulnerabilities of MM-MAS largely underexplored. To bridge this gap, we introduce HAM\textsuperscript 3 , a Hierarchical Attack framework for multi-modal multi-agent systems that decomposes attacks into three interconnected layers. Specifically, at the perception layer, HAM\textsuperscript 3 mounts attacks by perturbing visual inputs, textual inputs, and their fused visual-textual representations. At the communication layer, it performs communication-level attacks that corrupt message content and interaction topology, such as manipulating shared context or communication links to distort collective information flow. At the reasoning layer, it conducts reasoning-level attacks that interfere with each agent's cognitive pipeline, biasing reasoning trajectories and ultimately compromising final decisions. We evaluate HAM\textsuperscript 3 on the GQA benchmark through multi-agent systems built on distinct reasoning paradigms including ReAct, Plan-and-Solve, and Reflexion. Experiments demonstrate that our framework achieves an Attack Success Rate of up to 78.3%, with reasoning-layer attacks being the most effective. More than half of the successful attacks lead multiple agents to produce consistent errors. These findings offer valuable insights for building more robust and interpretable multi-agent intelligence.

cs.CV · cs.MA

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:fc5a3e92060dc1316c858bdf2e1318c8CVF · CVPR 2026

IBISAgent: Reinforcing Pixel-Level Visual Reasoning in MLLMs for Universal Biomedical Object Referring and Segmentation

Jiang, Yankai, Li, Qiaoru, Xu, Binlu, Sun, Haoran, Ding, Chao, Dong, Junting, Cai, Yuxiang, Zhang, Xuhong 等 9 位作者

原文摘要

Recent research on medical MLLMs has shifted its focus from image-level understanding to fine-grained, pixel-level comprehension. Although segmentation serves as the foundation for pixel-level understanding, existing approaches face two major challenges. First, they introduce implicit segmentation tokens and require joint fine-tuning of the MLLM and external pixel decoders, increasing the risk of catastrophic forgetting and limiting out-of-domain generalization. Second, most methods rely on single-pass reasoning and lack the ability to iteratively refine segmentation results. To overcome these limitations, we propose IBISAgent--a novel agentic MLLM that reformulates segmentation as a vision-centric, multi-step decision-making process. IBISAgent enables MLLMs to generate interleaved reasoning and text-based click actions, invoke segmentation tools, and produce high-quality masks without architectural modifications. We also design a two-stage training framework consisting of cold-start SFT and agentic RL with tailored, fine-grained rewards. Through iterative multi-turn visual reasoning, IBISAgent naturally facilitates mask refinement and enhances robustness in complex medical referring and reasoning segmentation tasks. Extensive experiments demonstrate that IBISAgent consistently outperforms both closed-source and open-source SOTA methods.

cs.CV · cs.AI

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗