IAG: Input-aware Backdoor Attack on VLM-based Visual Grounding
NeutralArtificial Intelligence
- A novel vulnerability in vision-language models (VLMs) has been identified through the introduction of IAG, a method that enables multi-target backdoor attacks on VLM-based visual grounding systems. This technique utilizes dynamically generated, input-aware triggers that are text-guided, allowing for imperceptible manipulation of visual inputs while maintaining normal performance on benign samples.
- The discovery of this vulnerability is significant as it highlights the security risks associated with VLMs, which have become increasingly integral in various applications, including image recognition and natural language processing. Addressing these vulnerabilities is crucial for ensuring the reliability and safety of AI systems that rely on visual grounding.
- This development underscores a growing concern in the AI community regarding the robustness of multimodal systems. As advancements in VLMs continue to enhance capabilities in spatial reasoning and image processing, the potential for malicious exploitation raises important questions about the ethical implications and security measures necessary to protect these technologies from backdoor attacks.
— via World Pulse Now AI Editorial System
