ASAP: Visual Analytics for Identifying and Analyzing Image Patterns in AI-generated Images
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 t...
| Authors | Jinbin Huang, Yuki Ueno, Chen Chen et al. |
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| Primary category | cs.HC |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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