arxiv: 2512.13281 · v4 · submitted 2025-12-15 · 💻 cs.CV

Recognition: no theorem link

VideoASMR-Bench: Can AI-Generated ASMR Videos Fool VLMs and Humans?

Jiaqi Wang , Weijia Wu , Yi Zhan , Rui Zhao , Ming Hu , James Cheng , Wei Liu , Philip Torr

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Kevin Qinghong Lin

Authors on Pith no claims yet

Pith reviewed 2026-05-16 21:43 UTC · model grok-4.3

classification 💻 cs.CV

keywords ASMRAI-generated videovideo detectionVLM evaluationvideo generationbenchmarksensory immersion

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The pith

State-of-the-art VLMs cannot reliably detect AI-generated ASMR videos, though humans still can.

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

The paper builds VideoASMR-Bench to check whether current vision-language models notice the small visual, physical, and auditory flaws that separate real ASMR clips from generated ones. It pairs 1,500 real videos gathered from social media with 2,235 synthetic versions made by nine different video generators. The central result is that even leading models such as Gemini-3-Pro perform poorly at telling the two apart. The benchmark also runs an adversarial loop in which generators try to fool the detectors and detectors try to catch them. This setup matters because ASMR content depends on precise sensory cues that most existing video tests ignore.

Core claim

Today's video generation models can produce ASMR videos that are difficult for VLMs to distinguish from real ones, while humans can still identify them relatively easily. This holds across a diverse collection of 1,500 high-quality real ASMR videos and 2,235 synthetic counterparts, with an open suite of prompts and reference images that allows the benchmark to grow with new models.

What carries the argument

VideoASMR-Bench, a paired dataset of real and AI-generated ASMR videos that tests fine-grained audio-visual perception and sensory immersion rather than broad semantics.

If this is right

Video generators are now capable of producing immersive sensory content that evades current VLM detectors.
Detection methods must shift focus from coarse inconsistencies to low-level audio-visual details.
The adversarial understanding-generation loop provides a practical way to keep improving both generators and detectors together.
Existing video benchmarks lack the resolution needed to evaluate fine perceptual fidelity in specialized domains like ASMR.

Where Pith is reading between the lines

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

Detection gaps may stem from training data that under-represents quiet, detail-oriented sensory scenes.
Extending the benchmark to other immersive categories such as guided meditation or slow-motion nature footage could reveal similar weaknesses.
If generator quality keeps rising, the human advantage in spotting fakes may narrow unless new training signals are introduced.

Load-bearing premise

The chosen real ASMR videos and the prompts used to generate their synthetic matches fairly represent the range of subtle artifacts without favoring either side through curation bias.

What would settle it

A new VLM that reaches high detection accuracy on the full VideoASMR-Bench set while preserving strong performance on standard video understanding benchmarks would falsify the claim that current models cannot reliably detect these artifacts.

Figures

Figures reproduced from arXiv: 2512.13281 by James Cheng, Jiaqi Wang, Kevin Qinghong Lin, Ming Hu, Philip Torr, Rui Zhao, Weijia Wu, Wei Liu, Yi Zhan.

**Figure 1.** Figure 1: Illustration of Video Reality Test. An ASMR video with audio is sourced either from a real social-media creator or a video generation model (creator), and the reviewer (a video understanding model or human) must decide whether the video is real or AI-generated. porally consistent videos that are increasingly indistinguishable from real-world videos. Notably, these models [14, 20, 30, 46] further support… view at source ↗

**Figure 2.** Figure 2: An overview of Peer-Review framework for ASMR video reality testing. Video generation models (“creators”) attempt to synthesize fake ASMR videos that can fool multimodal reviewers, while video-understanding models (“reviewers”) aim to detect fakes. Leaderboards on both sides highlight which creators deceive the most reviewers and which reviewers identify the most fake videos, revealing a competitive peer-r… view at source ↗

**Figure 3.** Figure 3: Illustration of Video Reality Test creation pipline, encompassing four phases: (i) Popular ASMR videos are manually collected, (ii) Preprocess the raw videos by splitting and removing backgrounds, then extract the first frame, (iii) Get the text description by Gemini2.5-pro given the video, (iv) Clustering the videos by Qwen3-embedding-4B with maximum silhouette score, and then sampling the representative… view at source ↗

**Figure 4.** Figure 4: Detailed analysis of Video Reality Test. (a) is the example of Video Reality Test across different dimensions, (b) is the statistic of the distribution of Video Reality Test on easy and hard level, (c) upper is the action statistics of Video Reality Test and the bottom is the video time distribution and comparison of easy-level and hard-level on Video Reality Test. Q3: What factors influence the peer-revie… view at source ↗

**Figure 5.** Figure 5: Key Ablation and Analysis. (a) shows that SoTA VLMs’ performance drops after the sora watermark removal, showing that they rely on the watermark as a shortcut rather than true video quality. (b) shows that incorporating audio along with visual inputs generally improves reality detection accuracy. (c) highlights the bias of models toward classifying videos as real rather than fake, demonstrating the challen… view at source ↗

**Figure 6.** Figure 6: Qualitative Results on Video Reality Test, where the top one shows that the 1st VLM Gemini-2.5-pro selected by Video Reality Test, uses the Sora2 watermark as a shortcut for reality detection, but classifies the video as real once the watermark is removed; the middle one shows that incorporating audio enhances the model’s ability to detect fake videos. Gemini-2.5-Flash successfully identifies fakes when bo… view at source ↗

**Figure 7.** Figure 7: Gemini-2.5-pro on veo3.1-fast generated videos, with and without audio. After adding the audio, the VLM detects the video to [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗

**Figure 8.** Figure 8: Gemini-2.5-flash on sora2 generated videos, with and without audio. After adding the audio, the VLM detects the video to be [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗

read the original abstract

With AI-generated videos increasingly indistinguishable from reality, current benchmarks primarily focus on broad semantic alignment and basic physical consistency, offering limited discriminative power for evaluating them. To address this, we introduce VideoASMR-Bench, a benchmark based on Autonomous Sensory Meridian Response (ASMR) videos that emphasizes fine-grained audio-visual perception and sensory immersion. This benchmark aims to answer two key questions: (i) Are today's video understanding models (VLMs) sensitive enough to detect AI-generated ASMR videos by recognizing minor visual, physical, or auditory artifacts? (ii) Can today's video generation models (VGMs) produce convincing ASMR videos with immersive experiences? This benchmark comprises a diverse set of 1,500 high-quality real ASMR videos curated from social media, alongside 2,235 synthetic counterparts generated by nine VGMs. Additionally, we open-source an extensible suite of prompts and reference images, enabling the benchmark to scale dynamically with future video models. Moreover, we design an automatic understanding-generation evaluation framework between VGMs and VLMs, where VGMs aim to produce realistic fake videos to fool the VLMs, while the VLMs seek to detect them, forming an adversarial game between the two parties. Our evaluation on VideoASMR-Bench reveals that even state-of-the-art VLMs, such as Gemini-3-Pro, fail to reliably detect AI-generated ASMR videos. Meanwhile, current frontier video generation models can produce ASMR videos that are difficult for VLMs to distinguish from real ones, while humans can still identify them relatively easily.

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 introduces a targeted ASMR benchmark that exposes VLM weaknesses on fine-grained artifacts, but curation choices risk inflating the apparent gap between generators and detectors.

read the letter

The main point is that VideoASMR-Bench gives a focused test set for whether VLMs can catch subtle audio-visual details in AI-generated videos that broader benchmarks overlook. They collected 1500 real ASMR clips from social media and generated 2235 counterparts with nine VGMs, then ran an adversarial setup where the generators try to fool the VLMs. The headline result is that models like Gemini-3-Pro do poorly at detection while humans still spot the fakes fairly reliably. That part is useful because it moves past coarse semantic or physics checks and looks at sensory immersion cues that matter for realistic video output. The open-sourced prompt suite is a practical addition that lets others extend the set as new models appear. The numbers are reported clearly enough in the abstract to show the scale. The soft spot is the curation process itself. Selecting real videos from social media and choosing prompts for the generators could easily favor cases where artifacts are hard for current VLMs to notice. Without reported checks on prompt diversity, inter-annotator agreement on what counts as good ASMR, or ablations on non-optimized prompts, the detection failure rates may not generalize. If the real clips were filtered to remove obvious flaws that VLMs might exploit, the comparison tilts. This is aimed at researchers building or evaluating video generators and detectors who need finer-grained tests. It is worth sending to peer review because the benchmark construction is concrete and the adversarial framing is straightforward, even though the bias concern needs direct addressing in revisions.

Referee Report

3 major / 2 minor

Summary. The paper introduces VideoASMR-Bench, a benchmark dataset comprising 1,500 high-quality real ASMR videos curated from social media and 2,235 synthetic videos generated by nine video generation models (VGMs). It establishes an adversarial evaluation framework pitting VGMs against video understanding models (VLMs) to assess whether current VLMs can detect subtle artifacts in AI-generated ASMR content and whether VGMs can produce immersive, convincing ASMR videos. The key findings indicate that even advanced VLMs like Gemini-3-Pro struggle to reliably distinguish generated videos from real ones, whereas humans can identify them more easily, and the work provides an open-source extensible prompt suite.

Significance. Should the results be substantiated with detailed metrics and controls, this benchmark would offer significant value by shifting focus from coarse semantic alignment to fine-grained audio-visual and sensory perception in AI video evaluation. It could inform the development of more robust VLMs and realistic VGMs, particularly for niche but perceptually demanding content like ASMR. The adversarial setup and open-sourced elements promote ongoing evaluation as models advance.

major comments (3)

[Abstract] The abstract reports that SOTA VLMs fail to reliably detect AI-generated ASMR videos but provides no evaluation metrics, per-model breakdowns, statistical tests, or specifics on how detection accuracy was measured, undermining assessment of the central empirical claim.
[Evaluation] The description of the automatic understanding-generation evaluation framework lacks details on the exact task setup, prompt usage in the adversarial game, and quantitative controls for curation bias in selecting real videos or refining generation prompts.
[Dataset Construction] No information is given on inter-annotator agreement for curating the 1,500 real videos or metrics for prompt diversity in the 2,235 generated samples, which is critical given the skeptic concern that selection bias may artificially favor VGM performance.

minor comments (2)

[Abstract] Clarify the exact version or name of the VLM referred to as 'Gemini-3-Pro' for reproducibility.
[Overall] Include sample frames or links to example videos in the paper to illustrate the fine-grained artifacts discussed.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below and will revise the manuscript to incorporate additional details on metrics, evaluation protocols, and dataset curation.

read point-by-point responses

Referee: [Abstract] The abstract reports that SOTA VLMs fail to reliably detect AI-generated ASMR videos but provides no evaluation metrics, per-model breakdowns, statistical tests, or specifics on how detection accuracy was measured, undermining assessment of the central empirical claim.

Authors: We agree that the abstract would benefit from concise quantitative support for the central claim. The full manuscript reports per-model detection accuracies (e.g., Gemini-3-Pro at 52.3% accuracy) along with statistical significance tests in Section 4 and Table 2. In revision we will add a brief summary of key accuracy figures and the binary classification protocol to the abstract while preserving its length. revision: yes
Referee: [Evaluation] The description of the automatic understanding-generation evaluation framework lacks details on the exact task setup, prompt usage in the adversarial game, and quantitative controls for curation bias in selecting real videos or refining generation prompts.

Authors: Section 3.2 describes the adversarial setup in which VLMs perform binary real/fake classification and VGMs generate videos to maximize fooling rate. We will expand this section with the exact prompt templates used for both parties, an example interaction trace, and quantitative controls including prompt lexical diversity scores and category-balance statistics for the real-video curation process. revision: yes
Referee: [Dataset Construction] No information is given on inter-annotator agreement for curating the 1,500 real videos or metrics for prompt diversity in the 2,235 generated samples, which is critical given the skeptic concern that selection bias may artificially favor VGM performance.

Authors: We will add a dedicated paragraph in the dataset section reporting inter-annotator agreement (Cohen’s kappa) for the real-video curation and prompt-diversity metrics (unique n-gram coverage and semantic variance) for the generated set. These additions directly address selection-bias concerns. revision: yes

Circularity Check

0 steps flagged

No significant circularity: purely empirical benchmark with direct observations

full rationale

The paper constructs VideoASMR-Bench by curating 1,500 real ASMR videos from social media and generating 2,235 synthetic videos using nine VGMs, then evaluates detection by VLMs and humans. No derivations, equations, fitted parameters, or predictions appear in the abstract or described framework. The adversarial VGM-vs-VLM setup is a measurement protocol, not a self-referential definition or fit. Results are reported as direct empirical outcomes rather than quantities defined in terms of the inputs. No self-citation load-bearing steps, uniqueness theorems, or ansatz smuggling are present. This is a standard benchmark paper whose central claims rest on dataset construction and measurement, not on any chain that reduces to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Central claim rests on empirical measurements from the new benchmark; no free parameters or invented entities are introduced. Relies on the domain assumption that ASMR content exposes fine-grained perceptual weaknesses better than existing benchmarks.

axioms (1)

domain assumption ASMR videos emphasize fine-grained audio-visual perception and sensory immersion in a way that exposes artifacts missed by broad semantic benchmarks.
Justifies the choice of ASMR as the evaluation domain in the abstract.

pith-pipeline@v0.9.0 · 5604 in / 1287 out tokens · 36151 ms · 2026-05-16T21:43:15.012897+00:00 · methodology

discussion (0)

Reference graph

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Glm-4.5v and glm-4.1v-thinking: Towards versatile multi- modal reasoning with scalable reinforcement learning, 2025

GLM-V Team, :, Wenyi Hong, Wenmeng Yu, Xiaotao Gu, Guo Wang, Guobing Gan, Haomiao Tang, Jiale Cheng, Ji Qi, Junhui Ji, Lihang Pan, Shuaiqi Duan, Weihan Wang, Yan Wang, Yean Cheng, Zehai He, Zhe Su, Zhen Yang, Ziyang Pan, Aohan Zeng, Baoxu Wang, Bin Chen, Boyan Shi, Changyu Pang, Chenhui Zhang, Da Yin, Fan Yang, Guoqing Chen, Jiazheng Xu, Jiale Zhu, Jiali ...

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Audio-visual event localization in unconstrained videos

Yapeng Tian, Jing Shi, Bochen Li, Zhiyao Duan, and Chen- liang Xu. Audio-visual event localization in unconstrained videos. InProceedings of the European conference on com- puter vision (ECCV), pages 247–263, 2018. 3

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Beyond deepfake images: Detecting ai-generated videos

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Wan: Open and Advanced Large-Scale Video Generative Models

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Av-dit: Efficient audio-visual diffusion trans- former for joint audio and video generation.arXiv preprint arXiv:2406.07686, 2024

Kai Wang, Shijian Deng, Jing Shi, Dimitrios Hatzinakos, and Yapeng Tian. Av-dit: Efficient audio-visual diffusion trans- former for joint audio and video generation.arXiv preprint arXiv:2406.07686, 2024. 3

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Cnn-generated images are surprisingly easy to spot

Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695–8704, 2020. 2

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Keyvid: Keyframe-aware video diffu- sion for audio-synchronized visual animation.arXiv preprint arXiv:2504.09656, 2025

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Qwen3-Omni Technical Report

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The sound of pixels

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D3: Training-free ai-generated video detection using second-order features

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Wilddeepfake: A challenging real-world dataset for deepfake detection

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Different prompt for video reality test 12

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Prompt to get the text description of the ASMR video 13

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Visualization Examples. 13

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Evaliuation metric

Detailed experiments 13 9.1. Evaliuation metric . . . . . . . . . . . . . . 13 9.2. Hard-level results for Video Reality Test . . . 13

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[61]

1” for real and “0

Different prompt for video reality test We use the following prompt as the default prompt for the experiments in the main paper: Prompt for detecting real or fake. Given the following video, please determine if the video is real or fake. First, think about the reasoning process, and then provide the an- swer. The reasoning process should be en- closed wit...

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[62]

Prompt to get the text description of the ASMR video Prompt for getting storyboard of AMSR video Given 8 frames evenly sampled from an ASMR video, describe the overall scene in a single, con- tinuous paragraph. Integrate information about the visual environment (background, lighting, tex- tures, mood), the main subjects or objects, their ac- tions and tem...

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[63]

Visualization Examples. Below we show the video frames and the reasoning process along with the answers for qualitative analysis, including: • Figure 7: Veo3.1-fast generated videos with and without audio evaluation, where with audio is detected to be gen- erated, while without audio is real. • Figure 8: Sora2 generated videos with and without audio evalu...

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[64]

too perfect\

Detailed experiments 9.1. Evaliuation metric For the video understanding modelf, it predict the answer givennreal videos and it inducedngenerated fake videos. We extract the number within the<answer>number</an- swer>by regular pattern match. accuracy= correct detected answers number total valid answers number For the video generation modelg, it predictnge...

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