ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images

2026-09-23

AI Summary

为解决AI生成图像中的欺骗性内容识别问题，本文提出ASAP系统，利用CLIP改编的图像编码器生成可解释表示，并通过可视化分析技术帮助用户分析和总结图像模式。

Abstract

Generative image models can produce highly realistic images, raising concerns about potential misuse in creating deceptive content. Current deepfake approaches face several challenges, including limited generalizability, lack of interpretability, and poor actionability. To help address these, we present ASAP, an interactive visualization system designed to empower users in the analysis and summarization of deceptive patterns in AI-generated images. ASAP introduces a novel CLIP-adapted image encoder that generates interpretable representations, enabling the extraction of influential pixel regions via calculated masks. This approach facilitates the identification of key deceptive features through influence measurement techniques. These backend techniques are integrated into a visual analytics dashboard that allows users to quantify and analyze authenticity-indicative patterns in image collections containing both authentic and AI-generated images. This approach also supports the comparative analysis of various generative models, including GANs and diffusion models. We demonstrate ASAP's efficacy through a user study and two application scenarios using established fake image detection benchmarks, showcasing its ability to effectively extract and quantify deceptive patterns.

Problem

• generative image models
• deceptive content
• deepfake approaches

Innovation

• interactive visualization system
• CLIP-adapted image encoder
• interpretable representations
• influence measurement techniques
• deceptive patterns