arXiv:2606.09132v1 Announce Type: new Abstract: Visual Language Models (VLMs) have gained significant popularity due to their remarkable ability. While various methods exist to enhance privacy in text-based applications, privacy risks associated with visual inputs remain largely overlooked such as Protected Health Information (PHI) in medical images. To tackle this problem, two key tasks: accurately localizing sensitive text and processing it to ensure privacy protection should be performed. To address this issue, we introduce VisShield (Vision Privacy Shield), an end-to-end framework designed
The proliferation of Vision Language Models and the increasing use of visual data, especially in sensitive domains like healthcare, necessitates immediate solutions for privacy preservation.
This development addresses a critical vulnerability in VLM applications, ensuring that the benefits of visual AI can be leveraged without compromising private information, particularly important for regulated industries.
The introduction of frameworks like VisShield enables more secure and compliant deployment of VLMs working with sensitive visual data, potentially broadening their applicability in privacy-conscious sectors.
- · Healthcare sector
- · AI/ML developers
- · Patients/Individuals
- · Privacy and compliance software providers
- · Unsecured visual data platforms
- · Adversarial actors seeking PII from visual data
VisShield provides a method to de-identify private information in visual data, enhancing privacy in VLM applications.
This capability will accelerate the adoption of VLMs in privacy-sensitive industries like healthcare and finance.
The increased trust and regulatory compliance of VLMs could lead to new applications and business models where visual data privacy was previously a barrier.
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Read at arXiv cs.AI