Visual Persuasion: What Influences Decisions of Vision-Language Models?

TL;DR

This paper introduces a framework for analyzing the visual preferences of vision-language models by systematically perturbing images and observing decision changes, revealing their vulnerabilities and biases.

Contribution

It develops a method to infer VLMs' visual utility through choice-based perturbations and visual prompt optimization, enabling interpretability and safety auditing.

Findings

Optimized visual edits significantly influence model choices.

Identified consistent visual themes affecting preferences.

Framework enables proactive safety and bias detection.

Abstract

The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs). These agents make visual decisions at scale, deciding what to click, recommend, or buy. Yet, we know little about the structure of their visual preferences. We introduce a framework for studying this by placing VLMs in controlled image-based choice tasks and systematically perturbing their inputs. Our key idea is to treat the agent's decision function as a latent visual utility that can be inferred through revealed preference: choices between systematically edited images. Starting from common images, such as product photos, we propose methods for visual prompt optimization, adapting text optimization methods to iteratively propose and apply visually plausible modifications using an image generation model (such as in composition, lighting, or background). We then evaluate which edits increase selection probability. Through large-scale experiments on frontier VLMs, we demonstrate that optimized edits significantly shift choice probabilities in head-to-head comparisons. We develop an automatic interpretability pipeline to explain these preferences, identifying consistent visual themes that drive selection. We argue that this approach offers a practical and efficient way to surface visual vulnerabilities, safety concerns that might otherwise be discovered implicitly in the wild, supporting more proactive auditing and governance of image-based AI agents.