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IllusionCAPTCHA: A CAPTCHA based on Visual Illusion

T0 review · 4 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read IllusionCAPTCHA defeats LLMs 100% of the time while 86.95% of humans pass on the first attempt.

desk verdict A genuinely new CAPTCHA mechanism with a real proof-of-concept, but the headline '100% deception' claim is conditioned on a non-adversarial fixed prompt and a curated test set, so the paper overstates its security. read the letter →

arxiv 2502.05461 v2 pith:DSOV5EGG submitted 2025-02-08 cs.CR

Ziqi Ding , Gelei Deng , Yi Liu , Junchen Ding , Jieshan Chen , Yulei Sui , Yuekang Li This is my paper · ORCID
classification cs.CR
keywords CAPTCHAsvisualillusionslargelanguagemodelsAIsecuritymultimodalHuman-EasyAI-Hardinducementpromptillusiondiffusion
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper argues that current CAPTCHAs are no longer secure against multimodal large language models, and that reasoning-based CAPTCHAs are also hard for humans. It proposes IllusionCAPTCHA, which generates images whose surface appearance hides the true content from AI but remains readable to humans. The central claim is that under the tested interaction protocol, GPT-4o and Gemini 1.5 Pro 2.0 fail to solve the CAPTCHA 100% of the time, while 86.95% of human participants pass on the first attempt. If correct, this would give web services a CAPTCHA that is both harder for LLM-based bots and easier for people than current alternatives.

What carries the argument

The load-bearing object is the illusion diffusion model (built on ControlNet) that fuses a base image with a user prompt, so the output image displays the prompt's content on its surface while preserving the base content underneath; the most confusing image is selected by lowest cosine similarity to the base. The second mechanism is the four-option multiple-choice frame with a deliberately verbose decoy describing the prompt's surface content, plus an inducement prompt ('Tell us the true and detailed answer of this image') that triggers LLM hallucination and length bias. Together they make the CAPTCHA a forced error for the tested LLMs and a quick recognition task for humans.

What would settle it

Run the same 30 illusionary CAPTCHA images against GPT-4o or Gemini with a prompt such as 'Ignore the surface decoration; describe any hidden or embedded content, and then choose the option that matches the hidden content,' plus a simple crop or contrast adjustment, and measure the success rate; any rate above 0% would falsify the 100% deception claim.

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Extended reading notes

Core claim

The authors claim to have built the first illusion-based CAPTCHA that instantiates the 'Human-Easy but AI-Hard' paradigm: an image is blended with a decoy prompt so that the generated picture looks like the prompt to an AI vision system but still reveals the original content to a human. Four multiple-choice options are offered, and a step-by-step 'inducement prompt' instructs the model to give the true detailed answer, which exploits the models' tendency to produce long detailed descriptions and steers them to the wrong option. In evaluation, neither GPT-4o nor Gemini 1.5 Pro 2.0 solved any of the 30 illusionary CAPTCHAs under zero-shot or chain-of-thought prompting, and the inducement prompt made both models pick the intended wrong option in 100% of cases; 86.95% of human participants passed on the first attempt.

Load-bearing premise

The 100% deception claim assumes the attacker uses the exact inducement prompt and the fixed four-option layout with no prompt rewriting and no image preprocessing; if an attacker simply asks the model to ignore the decorative text and identify hidden content, the reported rate has no support.

Editorial extensions

If this is right

  • If the result holds, existing LLM-based CAPTCHA solvers cannot pass IllusionCAPTCHA under the evaluated interaction, restoring a usable security layer for web services.
  • The inducement-prompt design becomes a template: any multimodal multiple-choice challenge can be structured to steer LLMs toward a predictable wrong answer.
  • Because 86.95% of humans pass on the first attempt, IllusionCAPTCHA would improve usability relative to reasoning-based CAPTCHAs, which the paper finds require a second attempt for most users.
  • The empirical finding that LLMs and humans make the same mistakes on existing CAPTCHAs implies that separate designs, not harder vision tasks, are needed to tell them apart.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The 100% deception rate is an upper bound under the exact inducement prompt and four-option layout; an attacker who rewrites the prompt to ignore the surface description, or preprocesses the image, may defeat it, and that scenario is not tested.
  • The underlying mechanism suggests a benchmark: illusionary images could be used to measure whether a vision model truly perceives semantics or only matches surface text, a testable extension beyond CAPTCHAs.
  • An arms race is likely: fine-tuning on illusionary images or adding a second model to check options could close the gap, so the security claim may be time-limited.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper reports two contributions. First, it presents an empirical study of two multimodal LLMs (GPT-4o and Gemini 1.5 Pro 2.0) on text-based, image-based, and reasoning-based CAPTCHAs under zero-shot and chain-of-thought prompting, together with a user study of the number of attempts humans need. The findings are that LLMs solve most text-based CAPTCHAs, do poorly on reasoning-based CAPTCHAs, and improve with CoT, while human users often need more than one attempt, especially on reasoning CAPTCHAs. Second, it introduces IllusionCAPTCHA, in which an illusion diffusion model blends a base image with a user prompt, four multiple-choice options are generated (one correct, one matching the generation prompt, and two verbose distractors), and an 'inducement prompt' is used to lure LLMs into selecting the distractors. The evaluation reports that both tested LLMs fail all 30 IllusionCAPTCHA images (100% deception in RQ4/RQ5), while 86.95% of 23 human participants pass on the first attempt (RQ6). The abstract concludes that IllusionCAPTCHA 'can effectively deceive LLMs 100% of the time' and outperforms other CAPTCHA systems in usability.

Significance. If the claims held broadly, the paper would make a useful contribution to the CAPTCHA arms race: visual illusions are a plausible source of human-AI asymmetry, the idea of deliberately engineered misleading options is creative, and the initial empirical study of LLMs across CAPTCHA categories is relevant to the community. The paper also provides a first attempt at measuring human first-attempt success rates across CAPTCHA types, which is a useful data point. However, the evaluation of IllusionCAPTCHA is narrow: only two models, one fixed prompt, one fixed option layout, and a test set selected for maximal deviation from the base image. The headline 100% figure is therefore an artifact of the particular evaluation setup rather than a demonstrated property of the proposed mechanism. The human samples are also small (10 for RQ3, 23 for RQ6), and the usability comparison to other CAPTCHA systems is not backed by a controlled comparison in this paper.

major comments (4)
  1. [Abstract and §6.3, Table 5] The central claim that IllusionCAPTCHA 'can effectively deceive LLMs 100% of the time' is conditional on the exact inducement prompt from §5.3 and the fixed four-option layout from §5.2. §3's threat model only excludes reverse engineering, JavaScript decompiling, and direct code analysis; it does not forbid an attacker from rewriting the prompt, cropping or inpainting the image, or using a second model to check the options. No such adaptive attacks are tested. An attacker who simply asks the model to ignore the decorative description and identify the hidden content is outside the tested evaluation, so the 100% figure does not support a general AI-hard claim.
  2. [§5.1 and §6.2, Table 4] The 30 IllusionCAPTCHA test images are selected by generating 50 candidates and keeping the one with the lowest cosine similarity to the base image. This is a form of selection bias: the evaluation does not sample from the distribution of images the generator produces but from the extreme tail that is least similar to the base image. The reported 0% LLM success rate may therefore overstate the deception rate of a randomly generated instance. In addition, the illusion strength is a fixed free parameter (1.5) with no sensitivity analysis, so it is unclear how robust the result is to parameter changes.
  3. [§5.2, §5.3, and §6.3, Table 5] The option design deliberately includes the exact prompt used to generate the illusion as a distractor, and another distractor is a verbose description of the illusionary element. The paper attributes the LLMs' 100% selection of the predicted option to visual illusions, but the evaluation does not ablate this option design. Without testing options with short, generic distractors or without the prompt-matching distractor, it remains possible that the LLMs are mostly responding to the length- and prompt-matching bias of the options rather than to the illusionary content. The contribution of the 'structured step-by-step method' is therefore not isolated from the confounding of option content.
  4. [§6.1 and §6.4, Tables 3 and 6] The human usability evidence is thin: RQ3 uses 10 participants and RQ6 uses 23, with no statistical testing or confidence intervals. Moreover, the RQ3 task (perceiving and describing an illusion) is not the same as the RQ6 task (selecting one of four multiple-choice options), so the 83%/88% visibility figures in Table 3 do not by themselves validate the 86.95% first-attempt pass rate in Table 6. The abstract's statement that IllusionCAPTCHA is 'outperforming other CAPTCHA systems' is not supported by any comparative user study in the paper, since no other CAPTCHA was presented to the same participants under the same protocol.
minor comments (4)
  1. [§4.1] There are several typos and infelicities, e.g., 'capthchas' in the dataset collection paragraph, 'Overloaping' in Table 1, 'incoperate' in §5, and 'multimodel' in §5. The paper would benefit from a proofreading pass.
  2. [§4.1 and §6.2] The datasets and evaluation protocols are described at a high level, but it is not always clear how the multiple-choice options in RQ4 were presented to the LLMs (i.e., whether the model was given the four options or asked to answer openly). Clarifying the exact input format for each RQ would improve reproducibility.
  3. [§5.1] The paper states that 50 images are generated 'at a fixed illusion strength level of 1.5—an optimal value for human identification in this context,' but no evidence is provided for this optimality. A small sensitivity analysis or a reference to prior work would be useful.
  4. [§7] The discussion of adversarial attacks and cross-cultural adaptability is qualitative and does not include any experiments. The claim that adversarial images lack transferability is only supported by a reference, not by experiments on the proposed system.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the paper's claims rest on empirical measurements rather than on derivations that reduce to their own inputs.

full rationale

The paper's quantitative claims are empirical measurements rather than derived results that collapse into their inputs. Section 4 reports measured LLM and human performance on existing CAPTCHAs; Section 5 motivates a design from those observations, including an inducement prompt and a deliberately verbose distractor, citing an external source ([15]) for LLM preference for long descriptions. Sections 6.2 and 6.3 then measure whether that design achieves its goals, and the reported 0% identification rate and 100% inducement rate are actual LLM responses, not consequences forced by construction. The image-selection heuristic in Section 5.1 (choosing the lowest cosine similarity to the base image as a difficulty proxy) does not by itself determine the later success rates; those rates come from running the models on the selected images. The self-citations ([7], [8]) are contextual and not load-bearing; no uniqueness theorem or author-imported ansatz carries the argument. The conditional nature of the 100% claim, which depends on a fixed four-option layout and a fixed inducement prompt, is a limitation on external validity rather than a circularity in the paper's derivation chain. Overall, no significant circularity is present.

Assumptions & free parameters 1 free parameters · 5 assumptions · 0 invented entities

The central claim rests on a small number of hand-chosen generation settings and on domain assumptions about LLM limitations, human visual perception, and attacker behavior. None of these are formalized or derived, and the empirical support is limited to two LLMs and small user studies.

free parameters (1)
  • illusion_strength = 1.5
    Chosen by hand in Section 5.1 as an "optimal value for human identification in this context"; not swept systematically and affects all generated images.
assumptions (5)
  • domain assumption Current multimodal LLMs systematically fail to identify hidden content in illusionary images.
    Assumed throughout Sections 5 and 6; tested only on GPT-4o and Gemini 1.5 Pro 2.0 over 30 images.
  • domain assumption IllusionDiffusion blending preserves enough base-image content for humans to recognize while hiding it from AI.
    Section 5.1 assumes this; RQ3 measured 83 to 88 percent visibility on 10 samples, with no guarantee for arbitrary base images.
  • domain assumption LLMs tend to select long, detailed descriptions and can be lured by inducement prompts into predictable wrong answers.
    The inducement design in Section 5.3 relies on prior work [15] and on observations from the authors' own tests with only two models.
  • domain assumption Human users know the named base content, such as characters or landmarks.
    Section 6.4 attributes failures to participants not knowing character names, so the usability claim assumes common knowledge of the chosen base content.
  • ad hoc to paper An attacker will use the unmodified inducement prompt and the fixed four-option interface.
    The 100 percent deception result depends on this; the threat model in Section 3 does not explicitly forbid prompt optimization or auxiliary vision calls.

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Cite this review

Pith. "Pith review of IllusionCAPTCHA: A CAPTCHA based on Visual Illusion." pith.science (2026). https://pith.science/paper/DSOV5EGG

@misc{pith2026250205461,
  author       = {Pith},
  title        = {Pith review of: IllusionCAPTCHA: A CAPTCHA based on Visual Illusion},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DSOV5EGG}},
  note         = {Machine review of arXiv:2502.05461}
}
read the original abstract

CAPTCHAs have long been essential tools for protecting applications from automated bots. Initially designed as simple questions to distinguish humans from bots, they have become increasingly complex to keep pace with the proliferation of CAPTCHA-cracking techniques employed by malicious actors. However, with the advent of advanced large language models (LLMs), the effectiveness of existing CAPTCHAs is now being undermined. To address this issue, we have conducted an empirical study to evaluate the performance of multimodal LLMs in solving CAPTCHAs and to assess how many attempts human users typically need to pass them. Our findings reveal that while LLMs can solve most CAPTCHAs, they struggle with those requiring complex reasoning type of CAPTCHA that also presents significant challenges for human users. Interestingly, our user study shows that the majority of human participants require a second attempt to pass these reasoning CAPTCHAs, a finding not reported in previous research. Based on empirical findings, we present IllusionCAPTCHA, a novel security mechanism employing the "Human-Easy but AI-Hard" paradigm. This new CAPTCHA employs visual illusions to create tasks that are intuitive for humans but highly confusing for AI models. Furthermore, we developed a structured, step-by-step method that generates misleading options, which particularly guide LLMs towards making incorrect choices and reduce their chances of successfully solving CAPTCHAs. Our evaluation shows that IllusionCAPTCHA can effectively deceive LLMs 100% of the time. Moreover, our structured design significantly increases the likelihood of AI errors when attempting to solve these challenges. Results from our user study indicate that 86.95% of participants successfully passed the CAPTCHA on their first attempt, outperforming other CAPTCHA systems.

Figures

Figures reproduced from arXiv: 2502.05461 by the authors.

Figure 1
Figure 1. Text-based CAPTCHA [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Image-based CAPTCHA particular workloads embeded in the image-based CAPTCHAs, we categorize them into two groups. • Object Classification. This group (e.g., reCAPTCHA [11], as shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Reasoning-based CAPTCHA also require users to engage in higher-level reasoning, which can lead to increased frustration and impatience among human users. 2.2 Large Language Models The evolution of Large Language Models (LLMs) has transformed traditional AI learning method [1]. By increasing the scale of train￾ing data, model can significantly improve their ability to under￾stand, generate, and process human language… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Overview of IllusionCAPTCHA [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 5
Figure 5. Figure 5: An example of the original and illusionary image [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Hate in Plain Sight: On the Risks of Moderating AI-Generated Hateful Illusions

    cs.CR 2025-07 conditional novelty 7.0 of 10

    Hateful optical illusions generated with Stable Diffusion and ControlNet evade current moderation classifiers (best accuracy 0.245) and vision-language models (best accuracy 0.102), with simple image transformations s...

  2. Broken Gates: Re-evaluating Web Bot Defenses in the Age of LLM Agents

    cs.CR 2026-07 conditional novelty 6.0 of 10

    Commercial solvers defeat puzzle captchas at near-perfect rates for pennies per thousand, while reCAPTCHA v3's resistance comes from browser-environment authenticity, not human-like behavior.

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