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

Jinbin Huang, Yuki Ueno, Chen Chen, Aditi Mishra, Bum Chul Kwon, Zhicheng Liu, Chris Bryan

arXiv:2609.27371v1cs.HCcs.CV

TL;DR

Deepfake detectors face limits in generalizability, interpretability, and actionability. ASAP combines a CLIP-adapted encoder with interactive visual analysis to identify and quantify deceptive image patterns. The authors report that it effectively supports interpretable pattern analysis, while noting scalability constraints in its layout system.

  • Problem

    Deepfake detectors can struggle with generalizability, interpretability, and actionability, including black-box predictions and differences among generative models.

    Source: Introduction · p. 1

  • Method

    ASAP combines an interpretable image encoder with coordinated visual analytics to analyze authenticity-indicative patterns and influential pixel regions in mixed image collections.

    Source: Introduction · pp. 1–2, Learning Interpretable Representations · p. 5, Pixel Relevance Computation and… · p. 5

  • Results

    Usage scenarios and a mixed-methods user study demonstrate that ASAP can effectively support interpretable analysis of patterns in AI-generated and real images.

    Source: Introduction · p. 2

  • Takeaways & Limitations

    ASAP links collection-level summaries to image evidence, helping users analyze and summarize deceptive patterns in authentic and synthetic image collections.

    Source: Introduction · p. 2

    Changing the grid size, especially for larger image ensembles, may create a computation bottleneck because IsoMatch has O(n^3) complexity.

    Source: Discussion and Conclusion · p. 11

Abstract

from arXiv · show

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.

Introduction

ASAP addresses deepfake detectors’ limits in generalizability, interpretability, and actionability with a human-in-the-loop system for analyzing patterns across authentic and AI-generated image collections. Its CLIP-based approach aims to connect image-level evidence with interpretable patterns and supports analysis by experts and other stakeholders.

Introduction

  • Deepfake detectors face challenges in generalizability, interpretability, and actionability, including failures across generative models and black-box predictions without explanations.The need for analysis extends to misinformation researchers, forensic analysts, moderators, journalists, and legal experts, not only AI specialists.Source: Introduction · p. 1
  • ASAP is a human-in-the-loop pipeline and visual analytics tool for identifying, analyzing, and summarizing patterns in mixed authentic and fake image collections.It is designed for settings with available authenticity labels and offers coordinated views from broad overviews to fine-grained details.Source: Introduction · pp. 1–2
  • ASAP links deceptive pixel patterns to classifier behavior through coordinated views, helping analysts examine why images succeed or fail detection.The interface combines collection-level summaries with image evidence, including misleading pixel groups shown in saliency maps.Source: Figure 1 · p. 2, Introduction · p. 2
  • A mixed-methods user study and usage scenarios show that ASAP can support interpretable analysis of patterns in AI-generated and real images.The authors also describe lessons on tailoring visualization-driven, human-in-the-loop approaches to deepfake analysis.Source: Introduction · p. 2

Detecting Fake Images

  • ASAP adapts CLIP’s visual encoder into an interpretable representation, building on CLIP feature spaces while addressing the text–vision entanglement that can impair visual authenticity analysis.Its orthogonal forget-to-spell projection minimizes textual content in CLIP’s latent space for fake-image detection and pattern discovery.Source: Detecting Fake Images · p. 2

Measuring Token Influence In Transformer Models

The paper adapts transformer token-relevance methods to analyze image evidence and situates ASAP among visual analytics tools for AI and GenAI. Its design responds to the need to identify and compare deceptive patterns and explain detector misclassifications.

Measuring Token Influence In Transformer Models

  • ASAP adapts Chefer et al.’s transformer relevance maps to CLIP by propagating from embedding dimensions rather than final logits, yielding finer-grained analysis than a single binary-classification map.For vision transformers, token relevance identifies influential pixels in model decisions.Source: Measuring Token Influence In… · p. 3

Visual Analytics for AI and Generative AI

  • Visual analytics and explainable-AI tools cover tasks from data exploration to model explanation, while GenAI tools often visualize model internals or support interaction with generative models.The cited examples include transformer attention, diffusion processes, and prompt engineering.Source: Visual Analytics for AI and Generative… · p. 3
  • ASAP connects collection summaries to pixel-level evidence behind detector decisions, addressing a gap left by painting-comparison tools that do not analyze nuanced real-versus-fake patterns.The comparison is with Ye et al.’s system for reviewing and comparing human- and AI-generated paintings [56].Source: Visual Analytics for AI and Generative… · p. 3

Design Challenges and Goals

  • Users need to explore mixed image ensembles, locate suspicious items, compare detector successes and failures, and understand where and why misclassifications occur.The design challenge is to extract patterns qualitatively and quantitatively while supporting intuitive, interpretable analysis at varying levels of detail.Source: Design Challenges and Goals · p. 3

Design Challenges

The design challenges concern discovering and quantifying deceptive pixel groups, managing their analysis, and adapting to diverse generative models. ASAP’s goals pair an interpretable classifier and gradient-based pixel analysis with comparable influence scores and multilevel visual exploration.

C1 Automatic Discovery of Patterns Present in Fake

  • The paper identifies extracting patterns that distinguish fake from real images and supporting intuitive, interpretable analysis at multiple levels as core challenges.C1 focuses on finding distinctive patterns amid irrelevant visual information; C2 notes that existing methods assess individual pixels and lack uniform cross-image metrics for pixel groups.Source: C1 Automatic Discovery of Patterns… · p. 3

C3 Efficient Analysis and Summarization of Fake

  • C3 highlights the risk that an overwhelming number of patterns can hinder analysis, with no established guidelines for designing an interface or tailoring the workflow.The interface must help users examine and summarize patterns.Source: C3 Efficient Analysis and… · pp. 3–4

Design Goals

  • G1 uses supervised learning on a frozen, large-scale pretrained encoder and distills its embeddings into orthogonal authenticity-related dimensions linked to classifier weights and pixel relevance maps.The classifier is intended to detect real versus fake images while encoding information useful for pattern discovery.Source: Design Goals · p. 4
  • G2 propagates gradients from each distilled dimension to CLIP attention maps, producing dimension-specific token relevance maps that are mapped to pixel sensitivities.The resulting masks trace pixel groups that affect authenticity classification.Source: Design Goals · p. 4

G3 Establish a uniform metric for evaluating pattern

  • G3 combines each image’s dimension value with its classifier weight into a normalized signed score, where sign indicates prediction direction and magnitude indicates influence strength.The score is intended to support comparisons across images, groups, and prediction outcomes.Source: G3 Establish a uniform metric for… · p. 4

ASAP Backend

  • G4 and G5 call for comparing correctly classified and misclassified images with shared visual traits and navigating from summaries to individual images.ASAP’s backend trains real/fake classifiers, uses them to encode images, and leverages the representations to uncover relevant patterns.Source: G3 Establish a uniform metric for… · p. 4, ASAP Backend · p. 4

Datasets

ASAP trains classifiers on GAN- and diffusion-generated image datasets and adapts CLIP to produce compact authenticity-focused representations. The authors report detection performance comparable to state-of-the-art baselines, then use the representations to identify and quantify influential pixels.

Datasets

  • The evaluation uses proGAN images paired with authentic PASCAL images and latent-diffusion human faces paired with real FFHQ faces.The proGAN dataset has 30,000 real and 30,000 fake images per class across 20 classes; the diffusion evaluation uses 20,000 fake faces and real faces from FFHQ, which contains 70,000 images.Source: Datasets · p. 4
  • ASAP achieves detection performance comparable to state-of-the-art baselines on benchmark datasets while also providing interpretable insights into patterns contributing to detection.The paper directs readers to the Appendix for benchmark performance details.Source: Datasets · p. 5

Learning Interpretable Representations

  • A forget-to-spell orthogonal projection filters unintended text-related information from CLIP embeddings, producing a more visually focused representation for authenticity analysis.The projection is trained with an orthogonality loss on natural images, text-only images, and images with overlaid text.Source: Learning Interpretable Representations · p. 5
  • The encoder combines CLIP features, the forget-to-spell projection, and a distiller layer to produce a compact representation rich in image-authenticity information.The described pipeline maps CLIP’s 512-dimensional features to a visual-centric 256-dimensional vector, then distills it to 16 orthogonal dimensions for classification.Source: Learning Interpretable Representations · p. 5

Representation Distillation

  • Supervised distillation condenses visual-centric features into 16 orthogonal dimensions, enabling gradient-based analysis of influential pixel groups and their impact.The authors describe 16 dimensions as an empirical balance between performance and explainability, while noting that other dimensional spaces are possible.Source: Representation Distillation · p. 5

Pixel Relevance Computation and Pattern Discovery

ASAP derives dimension-specific pixel masks from distilled authenticity representations, then assigns signed, normalized influence scores and clusters similar image regions into interpretable visual concepts.

Pixel Relevance Computation and Pattern Discovery

  • Sixteen distilled-representation dimensions produce distinct pixel masks, isolating influential regions and enabling fine-grained inspection of subtle artificiality cues.The distinct dimensions provide different relevance perspectives rather than a single aggregated saliency map.Source: Pixel Relevance Computation and… · p. 5

Identifying Influential Pixel Groups

  • For CLIP:ViT-B/32, token relevance is refined by backpropagating through transformer attention, then resized into 16 masks at the original image dimensions.The image is divided into 7 × 7 patches, with an additional [CLS] token; the final relevance computation uses the last transformer block.Source: Identifying Influential Pixel Groups · p. 6

A Uniform Metric for Pattern Contribution

  • A normalized contribution score in [-1, 1] combines local and global dimensional information, with sign indicating fake- versus real-supporting influence and magnitude its extent.The classifier’s weights are used to compute dimension-level contributions, enabling consistent comparisons within and across images.Source: A Uniform Metric for Pattern… · p. 6

Revealing Commonality of Similar Images

  • Similar images are grouped by proximity in distilled-representation space; masked segments are encoded and clustered to reveal shared visual concepts.The method excludes highly overlapping segments and uses three k-means clusters, selected as a balance for the tested datasets.Source: Revealing Commonality of Similar Images · p. 7

System Stack

ASAP separates its computation backend from its frontend: a Flask server handles analysis computations, while React and d3.js build the interface.

System Stack

  • A Flask server handles interpretable image embeddings, influential-pixel segmentation, and influence measurement in response to user queries.These are the listed computation tasks handled by the server.Source: System Stack · p. 7
  • The backend computes interpretable image embeddings for the interface.Source: System Stack · p. 7
  • The frontend interface is built with React and d3.js.Source: System Stack · p. 7

ASAP Interface

ASAP’s interface links a global representation overview to image-level and pattern-level inspection. Its overview aggregates classifier behavior by cell, while linked views support examining images and influential concepts.

ASAP Interface

  • Users select a generative model and subject, then analyze patterns at global, subset, and local levels through four linked views.Source: ASAP Interface · p. 7

Representation Overview

  • The Representation Overview places similar distilled representations in non-overlapping grid cells and summarizes each cell’s classifier performance with a glyph.The glyphs help users scan for undetected fake images; users can annotate cells, and select one to load its images into linked panels.Source: Representation Overview · p. 7
  • The overview glyph encodes true and false predictions by sector, image authenticity and prediction correctness by color, confidence by saturation, and example count by size.Hovering reveals exact values; selecting a cell loads its images for detailed analysis.Source: Representation Overview · p. 7, Figure 3 · p. 7

Image View

  • The Image View arranges selected-cell images by similarity and juxtaposes ground truth with detector predictions, while lassoed images can be examined in Pattern View.A Show Concept feature displays influential visual concepts for further inspection.Source: Image View · p. 8

Pattern View

ASAP’s Pattern and Dimension Views connect influential image pixels with quantitative dimension contributions; usage scenarios show these cues revealing factors associated with misclassification.

Pattern View

  • Each selected image is shown across 16 dimensions with pixel-relevance heatmaps, positive-fake and negative-real contribution bars, and a waterfall summary of dimension influences.The waterfall chart helps identify influential dimensions and their corresponding pixel groups.Source: Pattern View · p. 8

Dimension View

  • Dimension View compares distributions across 16 dimensions, ordering global real/fake distributions by KL divergence and overlaying a selected cell’s distribution for comparison.Contribution plots and filters let users compare correct and incorrect images to pinpoint dimensions linked to prediction differences.Source: Dimension View · p. 8

Usage Scenarios

  • In similar horse-head cells with different accuracy, dimension 2 contributions differed, and its pixel groups correlated with areas near the horse’s eyes.The scenario reports that horse eyes and ears can play a key role in fake-image detection, even when they appear similar across real and fake images.Source: Usage Scenarios · pp. 8–9, Figure 4 · p. 9
  • For LDM faces, realistic objects near faces, including glasses and microphones, influenced fake images toward real predictions; smiling-image errors often stemmed from the forehead.The mouth region contributed to a fake classification in misclassified smiling-face images.Source: Usage Scenarios · p. 9
  • Real faces were often misclassified based on hair associated with dimension 14 or background blur associated with dimension 1.Figure 5 identifies these as recurring patterns in real images flagged as synthetic.Source: Usage Scenarios · p. 9, Figure 5 · p. 9

User Study

The user study assessed whether ASAP supports fake-pattern summarization and analysis using quantitative cues, through training, two tasks, and a usability review.

User Study

  • Ten participants completed a mixed-methods study assessing pattern summarization and the use of quantitative model-derived cues to analyze specific fake-image patterns.The study collected quantitative and qualitative data and received institutional review board oversight and participant consent.Source: User Study · p. 10

Study Design

  • Training included a five-minute system video and a practice task identifying a fake pattern in bird images and reasoning about contributing dimensions.Participants could ask questions during the video and then used the task to familiarize themselves with ASAP.Source: Study Design · p. 10
  • In T1, participants identified five fake patterns across proGAN horse and LDM face images; completion time and correctness were recorded, with think-aloud used to examine workload.The task had no time limit and assessed accurate identification and description of actual patterns.Source: Study Design · p. 10
  • In T2, participants analyzed the “person-on-horse” pattern and specified relevant dimensions, with time and interpretation accuracy recorded; a post-task survey rated usability and features on seven-point scales.The review also collected participant comments.Source: Study Design · p. 10

Participants and Apparatus

All participants completed both study tasks, and survey and interview feedback was positive about ASAP’s usability and analysis features.

Participants and Apparatus

  • Eight CS graduate students, one post-doc, and one education-technology graduate student participated; all had generative-image experience and image-review proficiency.Participants’ mean age was 28.4 years (SD = 2.6); seven were male and three female.Source: Participants and Apparatus · p. 10

Study Results

  • All participants completed both tasks with the requested pattern labels and dimension-based explanations; survey feedback rated ASAP easy to use, effective for summarizing patterns, and supportive of analysis.Ratings for the overview, image, pattern, and dimension views were also high; Figure 7 summarizes survey ratings, with medians in brighter blue.Source: Study Results · p. 10, Figure 7 · p. 10
  • Participants used the Representation Overview and Image View to navigate image collections, often starting with large cells showing many misclassified fake images before closer review.Participants described the overview as efficient for browsing many images at once.Source: Study Results · p. 10

G5) Pattern View

  • Participants found Pattern View useful for focused analysis by highlighting influential pixel groups and measuring their impact.Comments described identifying fake patterns in backgrounds and in a person riding a horse.Source: G5) Pattern View · pp. 10–11
  • Participants linked fake patterns to dimension distributions, while finer-grained patterns varied across individuals despite consistent identification of more salient patterns.Participants also suggested enlarging images and adding natural-language pattern descriptions as possible improvements.Source: G5) Pattern View · p. 11

Discussion and Conclusion

ASAP combines backend techniques with interactive, human-in-the-loop visual analytics to address generalizability, interpretability, and actionability in AI-image analysis. It supports in-depth analysis of image patterns, while the evaluation and discussion identify practical scope and design constraints.

Discussion and Conclusion

  • Usage scenarios and a user study demonstrate ASAP’s effectiveness in supporting design goals G1–G5.The authors also identify future directions for visual analysis and explainability of GenAI images and models.Source: Discussion and Conclusion · p. 11
  • Detector capacity is bound by CLIP; alternative backbones require differentiable features and computable relevance maps, with non-transformers needing an adapted attribution method.For transformer encoders, swapping the backbone mainly requires retraining two small linear layers.Source: Discussion and Conclusion · p. 11
  • ASAP’s current post-hoc labeling supports analysis when ground-truth labels are known; real-time authenticity analysis is a future adaptation.The present setup is directed at datasets mixing AI-generated and real images.Source: Discussion and Conclusion · p. 11
  • Isomatch’s O(n^3) complexity can bottleneck larger or changing grids, while glyphs containing more images may require an Image View redesign.The default Representation View layout is precomputed for real-time interaction; tested glyphs usually aggregated at most 75 images.Source: Discussion and Conclusion · p. 11
  • Relevance maps locate influential regions but do not explain which visual attributes within those regions drive a prediction.A highlighted mouth, for example, does not distinguish shape, texture, boundary artifacts, or another property.Source: Discussion and Conclusion · p. 11
  • Interactive base-model selection is a proposed enhancement, while LLM-based verbal summaries depend on more mature vision capabilities or advanced RAG approaches.ASAP currently uses CLIP as its visual feature encoder.Source: Discussion and Conclusion · p. 11
  • The authors flag dual-use risks, face-image privacy concerns at scale, and broader concerns about generative-model bias and copyright.The privacy concern applies even when public benchmark datasets are used.Source: Discussion and Conclusion · pp. 11–12

Validating the Generalizability of the Detector

  • The detector outperforms many baselines and performs comparably to CLIP-based Ojha et al.23, while its interpretability-focused distillation discards some CLIP information.Generalizability is tested by training on proGAN and evaluating on unseen datasets.Source: Validating the Generalizability of the… · p. 15

Patterns of Confusing Real Horse Images

A comparison of nearby horse-image groups shows how ASAP can surface a feature associated with real images being misclassified as synthetic. Dimension 6 distinguishes the confusing 3D-game horses from correctly classified real horses in the scenario.

Patterns of Confusing Real Horse Images

  • Dimension 6 contributes significantly to synthetic classifications of real horse images from 3D games.The scenario identifies the horses as confusingly misclassified as fake.Source: Patterns of Confusing Real Horse Images · p. 15
  • Comparing O1 with nearby O2 reveals dimension 6 as a key differentiator despite both groups containing mostly real horses with grassy backgrounds.The images in O1 are mostly from 3D games; O2’s real images are correctly classified.Source: Patterns of Confusing Real Horse Images · p. 15
  • Dimension 6 focuses on pixels near the horse’s body or rider, predicting real images in O2 but failing for O1.Figure 8 marks the misclassified cell with a red border.Source: Figure 9 · p. 15, Figure 8 · p. 15

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