Multi-P$^2$A: A Multi-perspective Benchmark on Privacy Assessment for Large Vision-Language Models

Large Vision-Language Models (LVLMs) exhibit impressive potential across various tasks but also face significant privacy risks, limiting their practical applications. Current researches on privacy assessment for LVLMs is limited in scope, with gaps in both assessment dimensions and privacy categories. To bridge this gap, we propose Multi-P$^2$A, a comprehensive benchmark for evaluating the privacy preservation capabilities of LVLMs in terms of privacy awareness and leakage. Privacy awareness measures the model's ability to recognize the privacy sensitivity of input data, while privacy leakage assesses the risk of the model unintentionally disclosing privacy information in its output. We design a range of sub-tasks to thoroughly evaluate the model's privacy protection offered by LVLMs. Multi-P$^2$A covers 26 categories of personal privacy, 15 categories of trade secrets, and 18 categories of state secrets, totaling 31,962 samples. Based on Multi-P$^2$A, we evaluate the privacy preservation capabilities of 21 open-source and 2 closed-source LVLMs. Our results reveal that current LVLMs generally pose a high risk of facilitating privacy breaches, with vulnerabilities varying across personal privacy, trade secret, and state secret.

VLBiasBench: A Comprehensive Benchmark for Evaluating Bias in Large Vision-Language Model

The emergence of Large Vision-Language Models (LVLMs) marks significant strides towards achieving general artificial intelligence. However, these advancements are tempered by the outputs that often reflect biases, a concern not yet extensively investigated. Existing benchmarks are not sufficiently comprehensive in evaluating biases due to their limited data scale, single questioning format and narrow sources of bias. To address this problem, we introduce VLBiasBench, a benchmark aimed at evaluating biases in LVLMs comprehensively. In VLBiasBench, we construct a dataset encompassing nine distinct categories of social biases, including age, disability status, gender, nationality, physical appearance, race, religion, profession, social economic status and two intersectional bias categories (race x gender, and race x social economic status). To create a large-scale dataset, we use Stable Diffusion XL model to generate 46,848 high-quality images, which are combined with different questions to form 128,342 samples. These questions are categorized into open and close ended types, fully considering the sources of bias and comprehensively evaluating the biases of LVLM from multiple perspectives. We subsequently conduct extensive evaluations on 15 open-source models as well as one advanced closed-source model, providing some new insights into the biases revealing from these models. Our benchmark is available at https://github.com/Xiangkui-Cao/VLBiasBench.