arxiv: 2605.06143 · v1 · submitted 2026-05-07 · 💻 cs.CV · cs.AI

Recognition: unknown

AI-Generated Images: What Humans and Machines See When They Look at the Same Image

Silvia Poletti , Justin Ilyes , Marcel Hasenbalg , David Fischinger , Martin Boyer

Authors on Pith no claims yet

Pith reviewed 2026-05-08 13:56 UTC · model grok-4.3

classification 💻 cs.CV cs.AI

keywords AI-generated imagesexplainable AIfake image detectionhuman evaluationXAI methodstext-to-image generatorsvisual explanationsdisinformation detection

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The pith

Human surveys of 100 people identify which of 16 XAI methods best match human judgments on why images are flagged as AI-generated.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper trains a suite of detectors on a large photorealistic fake-image dataset called AIText2Image and then applies sixteen different explainable AI techniques to produce visual explanations for the detectors' decisions. These explanations are evaluated by collecting both visual and textual responses from a survey of 100 participants, with the explicit aim of measuring how closely the machine outputs align with human preferences and understanding. The authors argue that this alignment reveals useful visual-language cues that distinguish AI-generated images from real ones. A sympathetic reader would care because more human-aligned explanations could make detection tools more trustworthy and usable in real-world settings such as disinformation monitoring.

Core claim

Training detectors on AIText2Image and integrating sixteen XAI methods, then refining the resulting visual explanations through human survey responses, yields measurable alignment between machine outputs and human preferences; this process surfaces visual-language cues that both detectors and people rely on when identifying AI-generated images.

What carries the argument

The human-preference alignment evaluation that collects textual and visual responses from 100 participants to score how well each of the sixteen XAI outputs matches human understanding of AI-generated image cues.

If this is right

Detectors can be tuned to favor XAI methods that humans find clearest.
Identified visual-language cues can guide improvements in future detection architectures.
The alignment metric offers a concrete way to compare XAI techniques for any image-classification task.
Systems using the best-aligned explanations become more transparent for non-expert users.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same survey-based alignment procedure could be adapted to evaluate explanations in other AI domains such as text or audio generation.
Automating parts of the preference collection might allow continuous updating of which XAI methods are preferred as detectors improve.
The work implies that purely technical performance metrics are insufficient; human clarity must be treated as a primary design goal.

Load-bearing premise

That the judgments of 100 survey participants give a reliable and generalizable picture of how humans understand and prefer explanations for AI image detections.

What would settle it

A larger or demographically broader study that ranks the same sixteen XAI methods in a substantially different order of human clarity would falsify the alignment results.

Figures

Figures reproduced from arXiv: 2605.06143 by David Fischinger, Justin Ilyes, Marcel Hasenbalg, Martin Boyer, Silvia Poletti.

**Figure 1.** Figure 1: Overview of visual explanations of the XAI methods when applied to view at source ↗

**Figure 2.** Figure 2: XAI methods comparison based on the cosine similarity scores between view at source ↗

**Figure 3.** Figure 3: Survey image examples from four different generators along with the par view at source ↗

**Figure 4.** Figure 4: First row: XAI methods achieving average optimal similarity view at source ↗

**Figure 5.** Figure 5: (a) Each row represents a subset of images containing a specific item view at source ↗

**Figure 6.** Figure 6: Example of text-based human masks HC for different text categories C, indicated in blue. Again, R is equal to 8.8% of the image size, and α = 3. XAI methods achieve a significantly lower overall similarity to text-based human masks than view at source ↗

read the original abstract

The misuse of generative AI in online disinformation campaigns highlights the urgent need for transparent and explainable detection systems. In this work, we investigate how detectors for AI-generated images can be more effective in providing human-understandable explanations for their predictions. To this end, we develop a suite of detectors with various architectures and fine-tuning strategies, trained on our large-scale photorealistic fake image dataset, AIText2Image, and assess their performance on state-of-the-art text-to-image AI generators. We integrate 16 different explainable AI (XAI) methods into our detection framework, and the visual explanations are comprehensively refined and evaluated through a novel approach that prioritizes human understanding of AI-generated images, using both textual and visual responses collected from a survey of 100 participants. This framework offers insights into visual-language cues in fake image detection and into the clarity of XAI methods from a human perspective, measuring the alignment of XAI outputs with human preferences.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper adds a new photorealistic dataset and tests 16 XAI methods with a 100-person survey for human-aligned explanations in fake-image detection, but the survey lacks supporting details and no performance numbers appear.

read the letter

This paper introduces a new dataset called AIText2Image for photorealistic AI-generated images and combines it with a suite of detectors and 16 different XAI methods, evaluated partly through a human survey. The core idea is to make fake image detectors more transparent by aligning their explanations with what humans actually notice and understand. They train detectors with different setups on this dataset and check them against modern text-to-image tools. Then they apply the XAI techniques and use survey responses from 100 people, both text and visual, to refine and score how clear the explanations are. This gives some insights into visual cues that help spot fakes and which XAI approaches match human views better. The new dataset and the multi-method XAI integration are the fresh parts. It builds on existing detection research by adding the human preference layer, which is a useful direction for making these systems more usable in real scenarios like fighting disinformation. The main weakness is in the human evaluation side. The survey size is modest, and the abstract gives no info on participant selection, demographics, or how consistent their answers were. That leaves open whether the alignment scores reflect broad human understanding or just the sample at hand. Also, the lack of any reported detector accuracies or specific findings in the abstract makes it difficult to assess the overall effectiveness right away. This work would interest researchers in computer vision who deal with generative AI and explainability. Someone looking for datasets or ideas on human-centered XAI in detection could find value here. It should go to peer review. The approach is solid enough on paper to warrant feedback, though the authors will likely need to add more on the survey methodology and quantitative outcomes to make the claims convincing.

Referee Report

2 major / 1 minor

Summary. The paper develops a suite of AI-generated image detectors trained on the AIText2Image dataset, integrates 16 XAI methods into the detection pipeline, and evaluates the resulting visual explanations via textual and visual responses collected from a survey of 100 participants. The central claim is that this human-centered evaluation framework measures the alignment between XAI outputs and human preferences, yielding insights into visual-language cues for fake-image detection and the clarity of different XAI techniques.

Significance. A robust human-evaluation component for XAI in image forensics could help close the gap between automated detectors and human-interpretable explanations, which is relevant for combating disinformation. The multi-architecture detector suite and the explicit focus on human alignment are positive design choices. However, because the manuscript supplies neither quantitative detector performance numbers nor any tabulated survey results or statistical measures, it is not yet possible to judge whether the claimed insights are actually delivered.

major comments (2)

[Abstract] Abstract: the manuscript states that responses from a survey of 100 participants are used to 'refine and evaluate' the 16 XAI outputs and to 'measure the alignment' with human preferences, yet supplies no information on participant demographics, recruitment, task design, statistical power, or inter-rater agreement. Because the central claim that the framework 'prioritizes human understanding' rests on these responses being a reliable proxy, the omission is load-bearing.
[Abstract] Abstract: despite asserting that detectors are assessed on state-of-the-art text-to-image generators and that XAI explanations are 'comprehensively refined and evaluated,' the text contains no quantitative performance metrics, confusion matrices, alignment scores, or error analysis. Without these data it is impossible to determine whether the empirical results support the stated claims.

minor comments (1)

[Abstract] The abstract introduces the acronym XAI without an initial expansion; a single parenthetical definition on first use would improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback highlighting the need for greater transparency in our survey methodology and empirical results. We agree that these elements are critical to substantiate the central claims and will incorporate the requested details and metrics in the revised manuscript.

read point-by-point responses

Referee: [Abstract] Abstract: the manuscript states that responses from a survey of 100 participants are used to 'refine and evaluate' the 16 XAI outputs and to 'measure the alignment' with human preferences, yet supplies no information on participant demographics, recruitment, task design, statistical power, or inter-rater agreement. Because the central claim that the framework 'prioritizes human understanding' rests on these responses being a reliable proxy, the omission is load-bearing.

Authors: We acknowledge that the current manuscript provides only a high-level description of the survey in the abstract and methods without the requested specifics. In the revision, we will expand the survey methodology section to include participant demographics (age, gender, education, and AI familiarity distributions), recruitment details (via online platforms and academic networks with inclusion criteria), task design (rating scales for explanation clarity, relevance to generative artifacts, and perceived alignment, with example interfaces), a priori statistical power analysis (targeting 80% power), and inter-rater reliability measures (Fleiss' kappa and Cronbach's alpha). These additions will directly support the reliability of the human evaluation as a proxy for preferences. revision: yes
Referee: [Abstract] Abstract: despite asserting that detectors are assessed on state-of-the-art text-to-image generators and that XAI explanations are 'comprehensively refined and evaluated,' the text contains no quantitative performance metrics, confusion matrices, alignment scores, or error analysis. Without these data it is impossible to determine whether the empirical results support the stated claims.

Authors: We agree that the lack of quantitative metrics limits the ability to assess the empirical support for the claims. The manuscript prioritizes the human-centered framework but does not present specific detector performance numbers, matrices, or tabulated alignment scores. In the revision, we will add a dedicated results section with detector performance metrics (accuracy, precision, recall, F1 across architectures on the AIText2Image test set and external generators), confusion matrices, quantitative alignment scores (mean survey ratings per XAI method with standard deviations and significance tests), and an error analysis of misalignment cases. This will enable evaluation of the insights on visual-language cues and XAI clarity. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical pipeline relies on external survey data and standard training

full rationale

The paper trains detectors on the authors' AIText2Image dataset, applies 16 off-the-shelf XAI methods, collects fresh textual/visual responses from 100 participants, and computes alignment scores between XAI outputs and those responses. No equations, predictions, or central claims reduce by construction to fitted parameters, self-definitions, or prior self-citations. The survey constitutes independent external input rather than a renamed or fitted output of the model itself. The framework is therefore self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no details on free parameters, axioms, or invented entities; insufficient information available to populate the ledger.

pith-pipeline@v0.9.0 · 5477 in / 1040 out tokens · 26964 ms · 2026-05-08T13:56:14.183404+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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