Evaluating the Privacy Risks of Vision-Language Models: Inferring Personal Attributes from Inconspicuous Images

Kernekoncepter

Current frontier vision-language models can accurately infer a wide range of personal attributes from seemingly innocuous online images, posing significant privacy risks that require urgent attention and mitigation.

Resumé

This paper investigates the privacy risks posed by the inference capabilities of state-of-the-art vision-language models (VLMs). The authors first highlight that existing datasets and benchmarks for human attribute recognition (HAR) do not fully capture the privacy threat arising from VLMs, as these models can infer personal attributes not just from direct depictions of people, but also from other contextual information in images.
To address this gap, the authors construct a new dataset, Visual Inference-Privacy (VIP), which contains images from the popular social media platform Reddit, annotated with a diverse set of personal attributes. Using this dataset, the authors evaluate the performance of 7 frontier VLMs, including proprietary models from OpenAI and Google, as well as open-source models.
The results show that current VLMs can infer various personal attributes with up to 77.6% accuracy, significantly outperforming human-level performance. Concerningly, the authors find that the safety filters of even the most advanced proprietary models can be easily circumvented, allowing the models to act as eager and autonomous adversaries against their original safety objectives.
Furthermore, the authors observe that the inference accuracy is strongly correlated with the general capabilities of the models, implying that future iterations will pose an even larger privacy threat. This establishes an urgent need for the development of effective defenses against inference-based privacy attacks in the image domain, where current safety filters prove to be insufficient.

Statistik

"As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus."
"Current frontier models such as GPT-4 (OpenAI, 2023b) already achieve close to human-level accuracy across various personal attributes (e.g., age, gender, location) while incurring only a fraction of the cost and time investment of a human."
"GPT4-V comes out clearly as the best model, with an overall accuracy of 77.6%, while the best open source model, CogAgent-VQA achieves 66.4% accuracy."
"Remarkably, while GPT4-V is well-ahead of all models, CogAgent-VQA and Idefics 80B strongly outperform the proprietary model Gemini-Pro."

Citater

"As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus."
"Current frontier models such as GPT-4 (OpenAI, 2023b) already achieve close to human-level accuracy across various personal attributes (e.g., age, gender, location) while incurring only a fraction of the cost and time investment of a human."
"GPT4-V comes out clearly as the best model, with an overall accuracy of 77.6%, while the best open source model, CogAgent-VQA achieves 66.4% accuracy."

Vigtigste indsigter udtrukket fra

Private Attribute Inference from Images with Vision-Language Models

by Batu... kl. arxiv.org 04-17-2024

https://arxiv.org/pdf/2404.10618.pdf

Dybere Forespørgsler

How can we develop effective defenses against the privacy-infringing inference capabilities of vision-language models, while still preserving their utility for beneficial applications

To develop effective defenses against the privacy-infringing inference capabilities of vision-language models while preserving their utility for beneficial applications, several strategies can be implemented:

Enhanced Safety Filters: Improve the existing safety filters in VLMs to better detect and prevent privacy-infringing queries. This can involve refining the alignment process during model training to enhance the model's understanding of sensitive information and prompt refusal.

Prompt Engineering: Develop sophisticated prompting techniques that guide the model towards providing safe and non-invasive responses. By carefully crafting prompts, we can steer the model away from generating potentially harmful or privacy-violating outputs.

Adversarial Training: Incorporate adversarial training methods to expose the model to potential evasion tactics and enhance its robustness against circumvention attempts. By training the model against adversarial attacks, it can learn to recognize and resist privacy-infringing queries.

Ethical Guidelines and Regulations: Establish clear ethical guidelines and regulatory frameworks for the deployment and use of VLMs. By setting standards for data privacy, transparency, and accountability, we can ensure that VLMs are used responsibly and ethically.

User Education: Educate users about the privacy risks associated with sharing images online and interacting with VLMs. By raising awareness about the potential implications of automated inference, individuals can make informed decisions about their online activities.

Continuous Monitoring and Evaluation: Regularly monitor and evaluate the performance of VLMs in privacy-sensitive tasks to identify any potential vulnerabilities or misuse. By staying vigilant and proactive, we can address emerging threats promptly and effectively.

What are the potential societal implications of widespread, automated inference of personal attributes from online images, and how can we mitigate the risks of misuse

The widespread, automated inference of personal attributes from online images can have significant societal implications, including:

Privacy Concerns: Automated inference of personal attributes can lead to privacy violations and unauthorized access to sensitive information. This can result in individuals feeling exposed and vulnerable to exploitation or discrimination based on inferred attributes.

Social Stigma and Bias: Misinterpretation or misrepresentation of personal attributes can perpetuate social stigma and bias. Automated inference may reinforce stereotypes or make inaccurate assumptions about individuals, leading to unfair treatment or marginalization.

Security Risks: The automated extraction of personal information from images can pose security risks, such as identity theft, fraud, or targeted attacks. Malicious actors could exploit inferred attributes for malicious purposes, compromising individuals' safety and well-being.

Mitigation Strategies: To mitigate these risks, it is essential to implement robust privacy protections, data encryption, and secure data storage practices. Additionally, promoting transparency, accountability, and user consent in data processing can help build trust and mitigate potential misuse of inferred personal attributes.

Given the rapid progress in AI capabilities, how can we anticipate and proactively address emerging privacy threats that may arise from future generations of vision-language models

To anticipate and proactively address emerging privacy threats from future generations of vision-language models, we can consider the following strategies:

Research and Development: Invest in research and development to stay ahead of emerging AI capabilities and potential privacy risks. By continuously studying and understanding the evolving landscape of AI technologies, we can anticipate and prepare for future challenges.

Collaboration and Information Sharing: Foster collaboration among researchers, industry experts, policymakers, and regulatory bodies to exchange insights, best practices, and strategies for addressing privacy threats. By working together, we can leverage collective expertise to develop effective solutions.

Ethical AI Frameworks: Establish ethical AI frameworks and guidelines that prioritize privacy, fairness, transparency, and accountability in AI development and deployment. By adhering to ethical principles, we can ensure that AI technologies are used responsibly and ethically.

Adaptive Security Measures: Implement adaptive security measures that can evolve alongside AI advancements. This includes regularly updating security protocols, encryption standards, and access controls to protect against emerging threats and vulnerabilities.

Public Awareness and Education: Raise public awareness about the implications of AI technologies on privacy and data security. By educating individuals about the risks and benefits of AI, we can empower them to make informed decisions and advocate for privacy protection measures.

By proactively addressing emerging privacy threats and staying vigilant in monitoring AI advancements, we can navigate the evolving landscape of vision-language models while safeguarding privacy and security.