Visual system identifies deceptive patterns in ai generated images
ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images
Human-Computer InteractionComputer Vision and Pattern Recognition
Summary
AI can create images that look very real, which raises worries about fake content. The authors built a tool called ASAP that helps people find and understand tricky patterns in AI-made images. ASAP uses special image analysis techniques to highlight important parts of images that reveal if they are fake. It shows these patterns visually to help users compare real and AI-generated images, and different AI models. The tool was tested with users and standard benchmarks, proving it helps spot and study deceptive image features.
What this means in practice
- •For security teams: Detect and analyze subtle deceptive image features in security contexts using ASAP's visual analytics dashboard.
- •For social media platform moderators: Compare and identify AI image manipulations across various generative models to aid content moderation.
Authors
Jinbin Huang, Yuki Ueno, Chen Chen, Aditi Mishra, Bum Chul Kwon, Zhicheng Liu, Chris Bryan
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.