  Generative image models have emerged as a promising technology to produce
realistic images. Despite potential benefits, concerns grow about its misuse,
particularly in generating deceptive images that could raise significant
ethical, legal, and societal issues. Consequently, there is growing demand to
empower users to effectively discern and comprehend patterns of AI-generated
images. To this end, we developed ASAP, an interactive visualization system
that automatically extracts distinct patterns of AI-generated images and allows
users to interactively explore them via various views. To uncover fake
patterns, ASAP introduces a novel image encoder, adapted from CLIP, which
transforms images into compact "distilled" representations, enriched with
information for differentiating authentic and fake images. These
representations generate gradients that propagate back to the attention maps of
CLIP's transformer block. This process quantifies the relative importance of
each pixel to image authenticity or fakeness, exposing key deceptive patterns.
ASAP enables the at scale interactive analysis of these patterns through
multiple, coordinated visualizations. This includes a representation overview
with innovative cell glyphs to aid in the exploration and qualitative
evaluation of fake patterns across a vast array of images, as well as a pattern
view that displays authenticity-indicating patterns in images and quantifies
their impact. ASAP supports the analysis of cutting-edge generative models with
the latest architectures, including GAN-based models like proGAN and diffusion
models like the latent diffusion model. We demonstrate ASAP's usefulness
through two usage scenarios using multiple fake image detection benchmark
datasets, revealing its ability to identify and understand hidden patterns in
AI-generated images, especially in detecting fake human faces produced by
diffusion-based techniques.
