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Temporal Slice Learning for AI-Generated Video Detection with 400$\times$ Fewer FLOPs

Wei Shang, Zhiyuan Li, Shengfeng He

arXiv:2610.35148v1cs.CV

TL;DR

Weak, localized temporal inconsistencies make AI-generated videos difficult to detect, while full-video modeling can spend computation on redundant appearance. This paper detects them with frequency-aware temporal slices and a lightweight TSC-Net, reporting improved slice-domain separability and strong performance with much lower computational cost. Its stated limitation is that highly realistic videos with very weak temporal evolution may offer few discriminative slice cues.

  • Problem

    AI-generated video traces are often weak, spatially localized temporal inconsistencies that are difficult to perceive in isolated frames, while full-video modeling can incur substantial cost on redundant appearance.

    Source: §1 · p. 2

  • Method

    The method reorganizes informative spatial rows and columns over time into high- and low-frequency temporal slices, then models slice consistency with a lightweight network.

    Source: §1 · pp. 2–3

  • Results

    High-frequency temporal slices reach 0.770 AUROC and low-frequency slices reach 0.650 AUROC, versus patch-domain AUROCs of 0.502 and 0.525, respectively.

    Source: §1 · p. 3

  • Takeaways & Limitations

    Temporal slices provide a compact representation that preserves local evolution while suppressing redundant scene appearance.

    Source: §1 · p. 2

    For highly realistic Seedance (Film) videos with very weak temporal evolution, both high- and low-frequency slices may contain limited discriminative cues.

    Source: H Limitations and Future Work · p. 24

Abstract

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Recent advances in generative video models have improved visual realism, making detection increasingly dependent on subtle temporal inconsistencies. However, these signals are weak and spatially localized, while most of the video content is redundant. This creates a fundamental mismatch: existing detectors rely on heavy backbones to model full videos, which leads to high computational cost and limited scalability. In this work, we rethink AI-generated video detection from a representation perspective. Instead of modeling entire videos, we restructure them into compact temporal slice representations that isolate informative temporal evolution. We introduce a lightweight framework using frequency-aware temporal slice consistency learning, where informative spatial rows and columns are aggregated over time to form slice-domain inputs. This reparameterization suppresses redundant appearance while preserving discriminative temporal dynamics, enabling efficient and targeted detection. By constructing slices from both high- and low-frequency regions, our method exposes complementary temporal artifacts, including unstable details and abnormal motion smoothness. We instantiate this idea in a Temporal Slice Consistency Network, which integrates task-driven slice localization, hierarchical positional encoding, and a lightweight Transformer to model cross-slice and cross-frequency dependencies. We further introduce AI-Artist, a new benchmark with artist-curated videos from recent high-fidelity generators, including Seedance and Kling. Experiments on GenVideo and AI-Artist show that our method achieves strong performance while requiring over 400$\times$ fewer model-forward FLOPs and 3.6$\times$ faster model inference than the strong video-based baseline ReStraV (2.9$\times$ faster end to end, including preprocessing), enabling scalable and practical deployment.

1 Rethinking AI-Generated Video Detection

AI-generated video detection must identify weak, localized temporal inconsistencies without spending computation on redundant scene content. The paper uses frequency-aware temporal slices to expose those cues and reports strong detection performance with substantially lower computational cost.

1 Rethinking AI-Generated Video Detection

  • Weak generation traces such as flickering textures and unstable structures are spatially localized and difficult to perceive in isolated frames.Full-frame modeling can spend computation on normal appearance and dilute weak temporal signals, while static patches may miss local evolution over time.Source: §1 · p. 2
  • Temporal slices reorganize informative spatial rows and columns over time, preserving local evolution while suppressing redundant scene appearance.The method constructs slices from high- and low-frequency regions to expose different temporal irregularities.Source: §1 · pp. 2–3
  • 0.770 AUROC for high-frequency temporal slices and 0.650 AUROC for low-frequency slices improve on patch-domain AUROCs of 0.502 and 0.525, respectively.The slice-domain distributions are more separable than selected spatial patches, especially in high-frequency regions.Source: §1 · p. 3, Figure 2 · p. 3
  • The paper frames its approach as temporal slice consistency learning, using slices as the primary input representation rather than as a visualization or internal feature-difference operator.A prior face-forgery study used slice-like visualization and feature-level temporal differences [25].Source: §1 · p. 3
  • TSC-Net models cross-slice and cross-frequency dependencies with task-driven slice localization and assembly, hierarchical positional encoding, and a compact Transformer.The slice-domain representation is presented as a way to retain task-relevant temporal evidence while reducing full-video processing.Source: §1 · p. 3
  • AI-Artist adds artist-curated videos from recent high-fidelity generators, and experiments on GenVideo and AI-Artist report over 400× fewer model-forward FLOPs than ReStraV.The benchmark addresses concerns that existing benchmarks are increasingly saturated by earlier-generation video models.Source: §1 · p. 3

I. Temporal Slice Construction

The method converts an input video into compact temporal slices before applying lightweight Transformer-based consistency modeling. Its framework combines preprocessing, key-frame selection, frequency-aware localization, and multi-order slice assembly.

I. Temporal Slice Construction

  • The framework transforms an input video into compact temporal slice representations through preprocessing, key-frame selection, frequency-aware patch localization, and multi-order slice assembly.A lightweight Transformer then models cross-slice and cross-frequency dependencies for detection.Source: Figure 3 · p. 4
  • The construction stage produces structured slice-domain inputs, which are then used for temporal consistency modeling rather than full-video processing.The framework caption specifies the sequence from slice assembly to Transformer modeling.Source: Figure 3 · p. 4
  • TSC-Net combines task-driven slice localization and assembly with hierarchical positional encoding and cross-slice/cross-frequency dependency modeling.These components form the lightweight network described as the paper's detector.Source: §I · p. 4

2 Method

The method converts videos into compact, frequency-aware temporal slices and uses a lightweight Transformer to model their consistency. Preprocessing selects a temporally informative window and complementary high- and low-frequency regions, while the network organizes slice information for prediction.

2.1.1 Content-Preserving Preprocessing and Key-Frame Selection

  • The pipeline crops frames consistently, selects the frame with maximal temporal variation, and extracts a centered fixed-length window, padding shorter videos or discarding frames outside longer windows.The same crop is applied across frames to preserve temporal alignment.Source: §2.1.1 · pp. 4–5

2.1.2 Frequency-Aware Patch Localization

  • High- and low-frequency patches are localized in the selected key frame, then extracted from every windowed frame at fixed spatial locations to preserve correspondence over time.The patches are chosen to capture complementary frequency-band artifacts.Source: §2.1.2 · p. 5

2.1.3 Multi-Order Slice Assembly

  • For each frequency group, the method selects temporally informative rows and columns, stacks them across time, and forms slices alongside first- and second-order temporal differences.Slices are aligned by cropping or padding and stacked across temporal orders into row-wise and column-wise tensors.Source: §2.1.3 · p. 5

2.2 Temporal Slice Consistency Network

  • TSC-Net uses a lightweight Transformer to jointly model consistency within each spatial direction and complementary information across directions and frequency groups.The modeled relationships include distinctions between high- and low-frequency slices.Source: §2.2 · p. 5

2.2.1 Slice Tokenization

  • The method divides row-wise and column-wise slices into local patches, vectorizes each patch, and projects it into a d-dimensional token sequence.The sequence contains patches collected from both spatial directions.Source: §2.2.1 · p. 6

2.2.2 Frequency-Aware Hierarchical Positional Encoding

  • Each token receives fixed sinusoidal encodings for slice direction, slice index, and local position, plus a learnable embedding for its high- or low-frequency group.This separates fixed structural coordinates from an adaptively calibrated frequency-group identity.Source: §2.2.2 · p. 6

2.2.3 Transformer Encoding and Classification

  • A learnable classification token and positional embedding are prepended to the token sequence; an L-layer Transformer then uses the classification-token output for prediction.Training uses binary cross-entropy between the predicted value and ground-truth label.Source: §2.2.3 · p. 6

3 Experiments

Experiments evaluate generalization on unseen generators and the artist-curated AI-Artist benchmark, alongside efficiency, robustness, false-positive, and ablation analyses. TSC-Net leads GenVideo mean metrics while reducing model-forward computation.

3 Experiments

  • Training follows GenVideo’s 10-generator setup, while evaluation uses unseen GenVideo and AI-Artist data and reports ACC, AP, AUROC, and efficiency metrics.Source: §3 · pp. 6–7

3.1 Comparison with State-of-the-Art Methods on GenVideo

  • 0.9698 mean ACC, 0.9973 AP, and 0.9967 AUROC give TSC-Net the best means across GenVideo’s 10 datasets.Against ReStraV, the corresponding means are 0.9562 ACC, 0.9961 AP, and 0.9959 AUROC.Source: §3.1 · p. 7, Table 1 · p. 8
  • Over 400× fewer model-forward GFLOPs (0.109 vs. 44.20) and 2.9× faster end to end distinguish TSC-Net from ReStraV.The model inference is 3.6× faster; end-to-end time is 4.676 vs. 13.444 ms.Source: §3.1 · p. 8, Table 2 · p. 9

3.2 Evaluation on Our AI-Artist Dataset

  • On AI-Artist, TSC-Net achieves 0.8558 ACC and nearly 0.98 AP and AUROC, leading those ranking metrics despite TALL’s higher ACC.AI-Artist evaluates detectors on videos from recent high-fidelity generators.Source: §3.2 · pp. 8–9, Figure 4 · p. 9

3.3 Robustness to Post-processing Operations

  • Across Gaussian blur, JPEG compression, and Gaussian noise, TSC-Net achieves the highest AP and remains most stable across degradation levels.Source: §3.3 · p. 9, Figure 5 · p. 10

3.4 False Positives on Real Videos

3.5 Ablation Study

  • Select Patch and Resize underperform the full model; 4-Seg improves metrics but raises preprocessing time from 3.198 to 8.908 ms, so the single-window default is retained.Source: §3.5 · p. 10, Table 3 · p. 10

4 Conclusion

The paper presents frequency-aware temporal slice learning as a lightweight approach to AI-generated video detection. The related-work and motivation passages explain why compact temporal representations may better expose localized inconsistencies than full-frame or isolated-patch processing.

4 Conclusion

  • The framework reparameterizes videos into compact slice-domain representations that preserve local temporal evolution while suppressing redundant appearance, with TSC-Net modeling cross-slice and cross-frequency dependencies.It combines task-driven slice localization and assembly, hierarchical positional encoding, and lightweight Transformer-based modeling; experiments cover GenVideo and AI-Artist.Source: §4 · p. 10

A.1 AI-Generated Image Detection

  • Image detectors use low-level forensic cues, pretrained semantic representations, or both, but operate on individual frames and do not explicitly model temporal evolution.The related-work passage contrasts this with the paper’s compact frequency-aware temporal slices.Source: §A.1 · pp. 14–15

A.2 AI-Generated Video Detection

  • Existing video detectors commonly process full-frame sequences or reason temporally in pretrained feature space, whereas this method constructs frequency-aware temporal slices before feature extraction.The method treats slices as primary inputs and models cross-slice and cross-frequency dependencies with a lightweight Transformer.Source: §A.2 · p. 15

B Motivation of Temporal Slice Representation

  • Temporal slices make residual inconsistencies in recent generated videos more explicit, including abrupt transitions, distorted contours, and irregular fluctuations that can be subtle in individual frames.Figure 6 compares slices from an earlier generator, a recent commercial generator, and real video; the earlier sample shows blurring, jagged transitions, and unstable local structures.Source: B Motivation of Temporal Slice… · pp. 15–16, Figure 6 · p. 15

C.1 Collection details of AI-Artist

AI-Artist combines in-the-wild and self-generated videos from recent commercial generators. Its collection and evaluation setup pairs the generated videos with a fixed, balanced real-video subset.

C.1 Collection details of AI-Artist

  • AI-Artist contains 380 generated videos: 221 web-collected and 159 self-generated, combining realistic online distributions with controllable samples spanning subjects, actions, scenes, and camera motions.The benchmark’s collection strategy covers both realistic dissemination scenarios and systematically generated samples from recent frontier commercial models.Source: §C.1 · pp. 16–17
  • The web-collected branch includes videos from Seedance Film, Kling 3.0, and AI_artists, with candidates manually reviewed for visual validity and reasonable association with their claimed source.The branch is intended to reflect AI-generated videos shared by users and creators on public platforms.Source: §C.1 · p. 16
  • The self-generated branch uses five commercial models and diverse prompts covering subjects, actions, scenes, and camera motions.The models are Runway Gen-4.5, PixVerse V5.6, Hailuo 2.3, Seedance 2.0, and Luma Ray3.14; prompts were constructed with large-language-model assistance.Source: §C.1 · pp. 16–17
  • For binary evaluation, the 380 generated AI-Artist videos are paired with 380 real videos from the GenVideo OOD real-video split, using the same fixed real subset across detectors.Matching real-video counts per generated subset keeps class priors consistent for AP, AUROC, and ACC.Source: §C.1 · p. 17

C.2 Diversity and Realism of AI-Artist

AI-Artist presents a more challenging setting in which recent high-fidelity videos narrow patch-level distributional differences. On this benchmark, the method achieves the best average AP and AUROC among the compared methods.

C.2 Diversity and Realism of AI-Artist

  • Natural and AI-generated patch distributions on AI-Artist are almost indistinguishable in both frequency regions, while temporal slices show clearer separability, especially at high frequencies.Slice-domain separability is reduced relative to Fig. 2(b), particularly in low-frequency regions, reflecting the increased realism and temporal coherence of recent generators.Source: §C.2 · p. 17, Figure 7 · p. 17
  • The method achieves the best average AP and AUROC on AI-Artist and remains among the top-performing methods on most generator subsets.Many existing methods degrade, especially those relying heavily on local or low-level statistical cues, and strong baselines show instability across generators.Source: Table 5 · p. 18, §C.2 · p. 18
  • AI-Artist emphasizes diversity across generation systems and realism through recent high-quality videos with substantially reduced conventional artifacts.These design goals make it a more challenging and realistic benchmark for evaluating generalization.Source: §C.2 · p. 18
  • As patch-level distributional differences diminish, effective detection increasingly depends on structured temporal slice representations rather than isolated spatial statistics.The results identify this as a key trend highlighted by AI-Artist.Source: §C.2 · p. 18

D.1 Ablation on Key Hyperparameters

TSC-Net remains stable across a broad range of tested hyperparameters. The selected defaults balance overall performance and comparison conditions, while longer temporal windows show a trade-off for ACC.

D.1 Ablation on Key Hyperparameters

  • Performance remains stable across a broad range of settings, especially for mAP and mAUROC, indicating limited sensitivity to hyperparameter choices.The tested factors are Transformer layers, attention heads, sampled slices, and temporal window length.Source: §D.1 · p. 19
  • Three Transformer layers, four attention heads, and npick = 8 provide the reported favorable depth trade-off, sufficient head count, and best overall sampled-slice results.The authors use npick = 8 as the default setting.Source: §D.1 · p. 19
  • Longer temporal windows slightly improve mAP and mAUROC, but ACC decreases as the window grows, possibly because short videos require more padding.The authors use Tmax = 8 in all experiments, considering overall performance and fair comparison with prior video-based baselines.Source: §D.1 · p. 19

D.2 Ablation on Temporal Slice Representation

The ablations compare temporal-order representations and examine slice construction, frequency-aware components, and positional encoding. Combining temporal orders yields the strongest GenVideo results, while no single order consistently dominates across datasets and metrics.

D.2 Ablation on Temporal Slice Representation

  • No temporal order consistently dominates across datasets and metrics; raw slices reach 0.9651 ACC on GenVideo and 0.8618 ACC on AI-Artist, while first-order differences achieve the highest AI-Artist ACC among the variants.Second-order differences alone are weaker, especially on AI-Artist, likely because they amplify noise and local instability as well as temporal discrepancies.Source: §D.2 · p. 19
  • 0.9698 ACC, 0.9973 mAP, and 0.9967 mAUROC on GenVideo make the full multi-order model best among single-order variants, while it remains highly competitive on AI-Artist.Inference time remains comparable across temporal-order variants.Source: §D.2 · pp. 19–20

D.3 Ablation on Temporal Slice Construction and Positional Encoding

  • The lower block of Table 6 evaluates slice construction, frequency-aware components, and positional-encoding variants on GenVideo and AI-Artist.The supplied passage identifies the evaluation scope but does not report its outcomes.Source: §D.3 · p. 20, Table 6 · p. 20

Temporal Slice Construction.

The ablations support combining high- and low-frequency temporal slices and using hierarchical positional encoding. Slice construction and frequency-aware localization outperform patch-only or resized alternatives, while the best frequency band varies by benchmark.

Temporal Slice Construction

  • “Select Patch” and “Resize” perform worse than structured temporal slice construction and frequency-aware patch localization.Selecting informative patches without constructing slices does not expose temporal inconsistencies as effectively; global resizing can dilute subtle artifacts with redundant appearance information.Source: Temporal Slice Construction · p. 20

High- vs. Low-Frequency Temporal Slices

  • On AI-Artist, “Only LF” reaches 0.9816 mAP and 0.9795 mAUROC, but on GenVideo it obtains 0.9336 ACC versus 0.9698 ACC for the full model.The passage attributes the benchmark difference to coarse structural evolution being more discriminative for recent high-fidelity generators, while local texture artifacts remain important for other generators.Source: Temporal Slice Construction · p. 20
  • The full model combines complementary high- and low-frequency cues and achieves the best overall cross-benchmark trade-off.Low-frequency slices capture coarse structural and motion-related inconsistencies, while high-frequency slices capture fine-grained local artifacts.Source: Temporal Slice Construction · pp. 20–21

Positional Encoding

  • Hierarchical positional encoding achieves the best GenVideo results and strong threshold-free performance on AI-Artist; fixed and learnable variants do not consistently match it.It combines frozen structural encoding with a learnable frequency embedding.Source: Temporal Slice Construction · p. 21

D.4 Ablation on Slice Direction and Temporal Coverage

The ablations compare slice direction and temporal coverage, examine alternative localization, and report full-pipeline costs. Broader temporal coverage improves all six reported metrics, while the single-window design is simpler and faster to preprocess.

D.4 Ablation on Slice Direction and Temporal Coverage

  • The preferred slice direction varies by dataset: “Only Column” is slightly stronger on AI-Artist, while the joint row-and-column design is stronger on all GenVideo metrics.Using both directions avoids choosing one based on the test distribution, with model time increasing from about 1.46 ms to 1.478 ms.Source: §D.4 · p. 21
  • 4-Seg. outperforms both 1-Seg. MP and the single-window default on all six metrics, while increasing preprocessing time from 3.198 to 8.908 ms.Model time remains essentially unchanged; the single-segment design remains the simpler, more efficient operating point when accuracy is not the priority.Source: §D.4 · p. 21

D.5 Alternative Slice Localization

  • Grad-CAM localization underperforms the gradient-based design on GenVideo and increases parameters, computation, and model latency; discrete slice-selection steps prevent direct gradient backpropagation to the localizer.End-to-end optimization would require relaxing discrete selection or adding surrogate supervision, and the same computational cost cannot be guaranteed.Source: §D.5 · pp. 21–22

E Full-Pipeline Computational Cost

  • 405.5× fewer model-forward FLOPs and 3.6× faster model-forward inference than ReStraV; including preprocessing, TSC-Net is 2.9× faster end to end.With preprocessing included, TSC-Net uses 236× fewer total GFLOPs than ReStraV.Source: E Full-Pipeline Computational Cost · p. 22

F False Positives on Real Videos and Weak-Motion Analysis

On real-video evaluations, TSC-Net produces few false positives and remains comparable to ReStraV on a weak-motion subset. Its temporal slices distinguish evolving patterns qualitatively, but highly realistic videos with very limited temporal variation remain challenging.

F False Positives on Real Videos and Weak-Motion Analysis

  • 7 false positives among 10,000 real MSR-VTT clips correspond to 0.07% FPR and 99.93% specificity at a fixed threshold of 0.5.The evaluation uses generated videos as the positive class and does not tune thresholds on the test set.Source: F False Positives on Real Videos and… · p. 22, Table 9 · p. 23
  • TSC-Net produces at most 5 false positives (FPR 0.20%) in each overlapping quartile subset of real clips, with the highest count on complex high-frequency backgrounds.The subsets use fixed statistics on 8-frame windows as proxies, not manually verified semantic categories.Source: F False Positives on Real Videos and… · p. 22, Table 10 · p. 23
  • Patch-domain HF/LF AUROC scores of 0.502 and 0.525 rise to 0.770 and 0.650 for temporal slices, respectively.The analysis describes real and generated distributions as strongly overlapping in the patch domain; slices expose differences in how local patterns evolve over time.Source: F False Positives on Real Videos and… · p. 22
  • On the balanced weak-motion subset, TSC-Net reaches 0.9528 ACC, 0.9969 AP, and 0.9960 AUROC, compared with ReStraV’s 0.9552 ACC, 0.9957 AP, and 0.9953 AUROC.The subset contains the 2,500 lowest-variation videos from each of the real and generated classes.Source: F False Positives on Real Videos and… · p. 23

G Feature Space Visualization

  • In the Seedance failure example, subtle facial-expression changes and limited temporal variation make both frequency-band slices weakly discriminative, and all compared methods fail.The t-SNE visualization shows more compact intra-class clustering and clearer inter-class separation for TSC-Net, but is qualitative.Source: Figure 9 · p. 23, Figure 10 · p. 24, G Feature Space Visualization · p. 24

H Limitations and Future Work

The paper identifies limits in slice-based detection, including weak temporal cues in highly realistic videos, heuristic slice selection, and unmodeled complementary signals. It also discusses future extensions, societal risks and safeguards, and examples illustrating AI-Artist’s visual diversity.

H Limitations and Future Work

  • Highly realistic Seedance (Film) videos with subtle expression changes can leave both frequency slices with limited discriminative cues; slice selection is also heuristic, and semantic, audio, and metadata cues are not explicitly used.On a balanced set of 5,000 low-variation videos, the method remains comparable to ReStraV.Source: H Limitations and Future Work · p. 24
  • Future work includes adaptive, learnable slice selection, complementary cues for videos with weak temporal variation, and expanding AI-Artist as generators evolve.Suggested additions include semantic representations and multimodal signals.Source: H Limitations and Future Work · p. 24

I Potential Societal Impacts

  • The paper notes risks including an arms race, privacy or misuse concerns from synthetic content resembling real people or scenes, and false positives or negatives in high-stakes applications.The authors recommend research-only use of the dataset and deployment with human oversight, transparency, and evaluation across diverse scenarios.Source: I Potential Societal Impacts · p. 25

J Visualization of AI-Artist

NeurIPS Paper Checklist

The NeurIPS Paper Checklist records disclosures on claim scope, limitations, reproducibility, reporting, compute, ethics, and assets, and clarifies that the LLM is used only to construct and expand prompts for AI-Artist’s self-generated subset—not as a core detector component—so the [N/A] designation applies to core methods without important, original, or non-standard LLM use.

Claims and limitations

  • The checklist says the abstract and introduction’s claims align with the paper’s scope and are supported by its method and empirical results.It also reports that a dedicated limitations section discusses method scope, dataset and deployment limitations, and the need for evaluation on future generators and real-world perturbations.Source: NeurIPS Paper Checklist · p. 29

Theory assumptions and proofs

  • The paper makes no formal theoretical claims or theorem-proof contributions, instead presenting an insight-driven representation design supported by analysis and empirical validation.The checklist marks full assumptions and proofs as not applicable.Source: NeurIPS Paper Checklist · pp. 29–30

Reproducibility and code access

  • The checklist says the paper provides the details needed to reproduce its main results, but does not publicly release code because industry-collaboration agreements restrict it.It documents preprocessing, slice construction, architecture, hyperparameters, training, and evaluation; GenVideo data are public, and AI-Artist’s construction protocol and statistics are documented, with research release planned subject to usage terms.Source: NeurIPS Paper Checklist · pp. 30–31

Experimental setting/details

  • The paper specifies training and test details, including datasets and splits, preprocessing, metrics, hyperparameters, optimizer, schedule, batch size, iterations, and hardware.The reported settings include the GenVideo protocol, its OOD test set, and the AI-Artist dataset.Source: NeurIPS Paper Checklist · p. 31

Experiment statistical significance

  • The checklist marks statistical-significance reporting as No; its justification says error bars are not strictly necessary given extensive comparisons across datasets, generators, perturbations, and ablations.The supplied passages do not report error bars, confidence intervals, or significance tests.Source: NeurIPS Paper Checklist · p. 31

Experiments compute resources

  • The paper reports experiments on an NVIDIA RTX PRO 6000 GPU using PyTorch and gives model parameters, GFLOPs, and average inference time per video.The checklist marks compute-resource disclosure as Yes.Source: NeurIPS Paper Checklist · p. 32

Societal impact and safeguards

  • The paper discusses positive impacts on media authenticity, content moderation, and digital forensics alongside risks including misuse, privacy concerns, and false positives or negatives.It also describes responsible-use safeguards for AI-Artist and the detector, including human oversight, transparency, and evaluation across diverse scenarios.Source: NeurIPS Paper Checklist · pp. 32–33

Assets, human subjects, and LLM usage

  • The checklist says existing assets are credited and used under applicable constraints, and AI-Artist’s construction and dataset documentation will be provided through the project page.It marks human-subject research and IRB review as not applicable, and says LLM use is declared when it is an important or non-standard part of the core method.Source: NeurIPS Paper Checklist · pp. 33–34

NeurIPS Paper Checklist

  • The LLM only constructs and expands prompts for AI-Artist’s self-generated subset; it is not part of the detector, inference pipeline, or core methodological novelty.The checklist defines [N/A] for work whose core method does not involve LLMs as important, original, or non-standard components.Source: NeurIPS Paper Checklist · p. 35

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