arXiv:2506.03933v2 Announce Type: replace-cross Abstract: Vision Language Models (VLMs) have shown remarkable capabilities in multimodal understanding, yet their susceptibility to adversarial perturbations poses a significant threat to their reliability in real-world applications. Despite often being imperceptible to humans, these perturbations can drastically alter model outputs, leading to erroneous interpretations and decisions. This paper introduces DiffCAP, a novel diffusion-based purification strategy that can effectively neutralize adversarial corruptions in VLMs. We theoretically estab
The increasing sophistication of Vision Language Models (VLMs) and their deployment in critical applications necessitates robust defenses against adversarial attacks.
The susceptibility of VLMs to undetectable adversarial perturbations threatens their reliability and trustworthiness, hindering their widespread adoption in sensitive domains.
New purification strategies like DiffCAP can bolster VLM security, potentially enabling their use in more critical and high-stakes environments.
- · AI developers
- · Cybersecurity firms
- · Industries relying on VLM deployments
- · Adversarial attackers
- · Unsecured VLM applications
VLMs become more resilient to adversarial attacks, improving their accuracy in real-world scenarios.
Increased trust in VLM outputs could accelerate their integration into sensitive applications such as autonomous systems and medical diagnostics.
A robust defense against adversarial attacks may shift the focus of AI security research towards more complex, multi-modal attack vectors or novel forms of model manipulation.
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Read at arXiv cs.AI