Pith. sign in

REVIEW 2 major objections 53 references

Moir\'e Video Authentication: A Physical Signature Against AI Video Generation

T0 review · 2 major / 0 minor · reviewed 2026-07-13 · grok-4.5

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read Real cameras produce a Moiré phase–motion correlation that current video generators cannot match.

desk verdict Clean optical invariant for passive video authentication; the real-vs-AI gap is real but the AI pipeline is not apples-to-apples, so treat the effect size as a lower-bound demonstration rather than a finished detector. read the letter →

arxiv 2604.01654 v2 pith:DZLADQY3 submitted 2026-04-02 cs.CV cs.AIcs.MM

Yuan Qing , Kunyu Zheng , Lingxiao Li , Boqing Gong , Chang Xiao This is my paper · ORCID
classification cs.CVcs.AIcs.MM
keywords videoauthenticationMoirépatternwatermarkingphysical-basedforensicsAI-generateddetectionopticalsignature
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

AI video is now hard to tell from real footage by eye or by statistical detectors. This paper argues that a compact two-layer grating in the scene creates interference fringes whose phase must move in lockstep with the grating’s image displacement whenever a real camera records the scene. That linear coupling is fixed by optical geometry, not by training data, so a verifier can extract the two signals from the video alone and check their correlation. Real recordings cluster near perfect correlation; carefully engineered outputs from current generators do not. The result is a positive, physics-grounded authenticity check for any video that contains the physical signature, offered as a complement to digital watermarks and after-the-fact detectors.

What carries the argument

The Moiré motion invariant: after rotation compensation, the cumulative fringe phase sequence φ(t) and the translational image displacement sequence of the grating are linearly coupled with |ρ| ≈ 1, independent of viewing distance and of the fixed grating parameters.

What would settle it

Produce, from a leading image-to-video model conditioned on a real grating frame, a short clip whose extracted fringe-phase and translational-displacement sequences achieve Pearson |ρ| in the same high range as real recordings (roughly above 0.8) under the paper’s pipeline.

Watch

Extended reading notes

Core claim

Under real image formation, fringe phase change and translational image displacement of a two-layer grating are linearly coupled with near-unity absolute correlation (the Moiré motion invariant). Real videos realize this correlation; AI-generated videos, even when conditioned on a real first frame containing authentic fringes, do not.

Load-bearing premise

Current and near-term video generators cannot solve or closely approximate the two-layer optical parallax equations that produce the invariant, even when given a real first frame and extensive prompt engineering.

Editorial extensions

If this is right

  • A wearable or badge-sized two-layer grating can serve as a passive physical authenticity token for press conferences, video calls, and other high-stakes recordings.
  • Verification needs only ordinary video plus the extracted phase and displacement signals; no camera calibration or known scene distance is required.
  • Splicing a real Moiré clip into a different motion trajectory fails the correlation test by construction.
  • The same invariant idea can be layered with existing face-swap and deepfake detectors to cover both full generative forgery and localized editing.
  • Multi-orientation gratings would extend sensitivity to arbitrary motion axes and raise the bar for any future generator that tries to forge the signature.

Reading between the lines

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

  • If generators later incorporate explicit ray-tracing of the two-layer geometry, the authentication guarantee would collapse unless the physical structure itself is made identity-binding or multi-axis.
  • The same optical-coupling principle could be applied to other deterministic physical effects (structured light, polarization, caustics) that statistical models do not currently solve frame-by-frame.
  • Requiring a brief “wave the badge” motion during video calls would force any face-swap attacker to either break the invariant or leave visible artifacts around the face.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 0 minor

Summary. The paper proposes a passive, physics-based video authentication signature based on the Moiré effect from a compact two-layer grating (lenticular sheet over a printed grating). From classical Moiré theory and a pinhole model it derives a Moiré motion invariant (Eqs. 1–4): under real image formation, fringe phase change Δφ(t) and translational image displacement of the assembly Δu_trans(t) are linearly coupled with near-unity absolute correlation, independent of distance and of the unknown proportionality constant. A proof-of-concept pipeline localizes the assembly (ArUco + PnP for rotation compensation), extracts phase via 1D FFT, and reports sliding-window Pearson correlation. Real videos (n=87) and Blender Cycles renders (n=70) yield high |ρ|; curated I2V videos from Veo 3.1, Grok Imagine, and LTX-2 (n=92 after discarding most of 321 generations) yield substantially lower |ρ|, with a large statistical separation. The authors argue that current generators do not solve the two-layer parallax equations and therefore cannot forge the invariant.

Significance. If the invariant holds under matched extraction and the modeling-gap assumption remains true for near-term generators, the work supplies a rare positive, physically grounded authentication signal rather than another artifact detector locked in an arms race. Strengths include a clean, parameter-free derivation that cancels distance, controlled physics renders that recover near-perfect correlation, explicit threat analysis (splicing, face-swap), and a practical off-the-shelf assembly. The contribution is complementary to deepfake detectors and digital watermarks and points to a broader class of deterministic optical signatures. The quantitative claim against AI generators is currently the load-bearing empirical pillar and needs tighter measurement conditions before the separation can be treated as fully established.

major comments (2)
  1. §4.3 vs. §3: Real videos use ArUco + PnP to isolate translational displacement Δu_trans; AI videos replace this with manual corner seeding + Lucas–Kanade optical flow and human correction, without the same rotation compensation. Because the invariant (Eq. 4) is defined on Δu_trans, not raw image motion, a non-negligible fraction of the reported ρ gap (μ≈0.91 vs. ≈0.34) could be an extraction mismatch rather than pure optical failure of the generators. Re-run the AI set under the identical PnP pipeline (or an automated surrogate that recovers pose from the same four corners when visible) and report the resulting ρ distribution; without that, the central quantitative claim is not measured under matched conditions.
  2. §4.3 curation: 229 of 321 I2V generations were discarded for severe temporal inconsistency before correlation was computed, and the retained 92 were further assisted by manual tracking. The paper correctly notes that this is a generous protocol for the attacker, but the published separation is then conditioned on human-selected, trackable clips. Report the full (pre-curation) distribution, or at least the fraction of generations for which any automated pipeline can extract both signals at all; otherwise the practical false-accept rate against unconstrained generation remains unclear.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Moiré motion invariant is a geometric elimination of unknowns, and correlation is measured rather than fitted.

full rationale

The load-bearing claim is the Moiré motion invariant (Eqs. 1–4): fringe phase change Δφ and translational image displacement Δu_trans are linearly coupled with |ρ|≈1 under real optics, independent of distance D. The derivation proceeds from classical two-layer Moiré beat geometry (Amidror/Kafri/Takasaki textbooks) plus pinhole parallax: apparent layer shift δ = Δx_c·g/D, phase Δφ ∝ δ/p_r, image translation Δu_trans = (f/D)Δx_c; eliminating D yields Δφ ∝ Δu_trans with fixed slope set by g, p_r, f. Rotation is argued not to induce inter-layer parallax, so only the translational component enters. No free parameter is fitted to data and then re-used as a “prediction”; the verifier simply extracts both sequences and tests Pearson correlation. Real, Blender, and AI videos are evaluated under that fixed criterion; the reported μ_ρ gap is an empirical outcome, not a quantity forced by construction. Self-citations (MoiréBoard/MoiréTracker/MoiréVision) appear only in Related Work as application contrast and do not underwrite the invariant. Window length and approximate intrinsics are evaluation/implementation choices, not inputs that redefine the claimed coupling. The derivation is therefore self-contained against external optical first principles and does not reduce to its own measurements.

Assumptions & free parameters 2 free parameters · 4 assumptions · 2 invented entities

Central claim rests on classical Moiré optics, the pinhole camera model, and the empirical modeling gap of current generators. No fitted constants enter the invariant itself; evaluation choices (window length, approximate f) affect only reported numbers, not the existence of the coupling.

free parameters (2)
  • sliding-window length = 30 frames
    30-frame windows used for per-window correlation curves; choice affects reported statistics but not the invariant derivation.
  • approximate camera intrinsics = fx=fy ≈ 0.5 W
    fx=fy=0.5·image_width used for PnP; not calibrated per device.
assumptions (4)
  • domain assumption Classical two-layer Moiré fringe period and phase-shift relation (Amidror, Kafri et al.)
    Used as starting point for Eq. 1; standard optical metrology result.
  • domain assumption Pinhole camera model with small-angle rotation producing pure image shift without inter-layer parallax
    Sec. 2.2; allows isolation of translational component via PnP.
  • ad hoc to paper Current video generators perform statistical synthesis and do not solve the two-layer optical parallax equations
    Load-bearing modeling assumption stated in Introduction and Conclusion; not independently proven for future models.
  • domain assumption Gap d ≪ Z so that the (1−d/Z) factor can be neglected
    Explicit approximation after Eq. 1; holds for mm-scale gap and meter-scale distance.
invented entities (2)
  • Moiré motion invariant (ρ between Δφ and Δu_trans) independent evidence
    purpose: Distance-independent, calibration-free authenticity test extractable from video alone
    Derived quantity, not a new physical object; independent evidence is the real-video correlation measurements themselves.
  • Compact two-layer grating assembly (lenticular + printed grating + ArUco) independent evidence
    purpose: Physical carrier that produces the observable signature under ordinary camera motion
    Engineering embodiment of known Moiré optics; independent evidence is the hardware BOM and real captures.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Moir\'e Video Authentication: A Physical Signature Against AI Video Generation." pith.science (2026). https://pith.science/paper/DZLADQY3

@misc{pith2026260401654,
  author       = {Pith},
  title        = {Pith review of: Moir\'e Video Authentication: A Physical Signature Against AI Video Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DZLADQY3}},
  note         = {Machine review of arXiv:2604.01654}
}
read the original abstract

Recent advances in video generation have made AI-synthesized content increasingly difficult to distinguish from real footage. We propose a physics-based authentication signature that real cameras produce naturally, but that generative models cannot faithfully reproduce. Our approach exploits the Moir\'e effect: the interference fringes formed when a camera views a compact two-layer grating structure. We derive the Moir\'e motion invariant, showing that fringe phase and grating image displacement are linearly coupled by optical geometry, independent of viewing distance and grating structure. A verifier extracts both signals from video and tests their correlation. We validate the invariant on both real-captured and AI-generated videos from multiple state-of-the-art generators, and find that real and AI-generated videos produce significantly different correlation signatures, suggesting a robust means of differentiating them. Our work demonstrates that deterministic optical phenomena can serve as physically grounded, verifiable signatures against AI-generated video.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

53 extracted references · 3 canonical work pages

  1. [1]

    Springer, 2nd edn

    Amidror, I.: The Theory of the Moiré Phenomenon, Volume I: Periodic Layers. Springer, 2nd edn. (2009)

    2009

  2. [2]

    BBC: Disgraceful deep-fake AI video condemned by presidential candidate (2025)

    2025

  3. [3]

    Blender Foundation: Blender: Open source 3d creation suite (2025),https:// www.blender.org

    2025

  4. [4]

    Bouguet, J.Y.: Pyramidal implementation of the Lucas Kanade feature tracker: De- scription of the algorithm. Tech. rep., Intel Corporation, Microprocessor Research Labs (2001)

    2001

  5. [5]

    OpenAI Technical Report (2024)

    Brooks, T., Peebles, B., Holmes, C., DePue, W., Guo, Y., Jing, L., Schnurr, D., Taylor, J., Luhman, T., Luhman, E., Ng, C., Wang, R., Ramesh, A.: Video gener- ation models as world simulators. OpenAI Technical Report (2024)

    2024

  6. [6]

    ByteDance Seed (2026) 16 Y

    ByteDance Seed Team: Seedance 2.0. ByteDance Seed (2026) 16 Y. Qing et al

    2026

  7. [7]

    In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems

    Campos Zamora, D., Dogan, M.D., Siu, A.F., Koh, E., Xiao, C.: MoiréWidgets: High-precision, passive tangible interfaces via moiré effect. In: Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems. pp. 1–10 (2024)

    2024

  8. [8]

    CNN: Finance worker pays out $25 million after video call with deepfake chief financial officer (2024)

    2024

Show all 53 references
  1. [9]

    C2PA (2022)

    Coalition for Content Provenance and Authenticity (C2PA): C2PA technical spec- ification. C2PA (2022)

    2022

  2. [10]

    arXiv preprint arXiv:2411.19537 (2024)

    Croitoru, F.A., Hiji, A.I., Hondru, V., Ristea, N.C., Irofti, P., Popescu, M., Rusu, C., Ionescu, R.T., Khan, F.S., Shah, M.: Deepfake media generation and detection in the generative AI era: A survey and outlook. arXiv preprint arXiv:2411.19537 (2024)

    2024 arXiv

  3. [11]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

    Esser, P., Chiu, J., Atighehchian, P., Granskog, J., Germanidis, A.: Structure and content-guided video synthesis with diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7346–7356. IEEE (2023)

    2023

  4. [12]

    GitHub (2024)

    FaceFusion: FaceFusion: Industry leading face manipulation platform. GitHub (2024)

    2024

  5. [13]

    arXiv preprint arXiv:2207.13744 (2022)

    Farid, H.: Lighting (in) consistency of paint by text. arXiv preprint arXiv:2207.13744 (2022)

    2022 arXiv

  6. [14]

    arXiv preprint arXiv:physics/0703098 (2007)

    Gabrielyan, E.: The basics of line moiré patterns and optical speedup. arXiv preprint arXiv:physics/0703098 (2007)

    2007 arXiv

  7. [15]

    Google DeepMind (2023)

    Google DeepMind: SynthID: Identifying AI-generated content. Google DeepMind (2023)

    2023

  8. [16]

    Google DeepMind: Veo 3.1 (2025),https://deepmind.google/

    2025

  9. [17]

    In: ICME (2021)

    Gragnaniello, D., Cozzolino, D., Marra, F., Poggi, G., Verdoliva, L.: Are GAN generated images easy to detect? A critical analysis of the state-of-the-art. In: ICME (2021)

    2021

  10. [18]

    In: IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP)

    Guo, H., Hu, S., Wang, X., Chang, M.C., Lyu, S.: Eyes tell all: Irregular pupil shapes reveal GAN-generated faces. In: IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP). pp. 2904–2908. IEEE (2022)

    2022

  11. [19]

    arXiv preprint arXiv:2601.03233 (2026)

    HaCohen, Y., Brazowski, B., Chiprut, N., Bitterman, Y., Kvochko, A., Berkowitz, A., Shalem, D., Lifschitz, D., Moshe, D., Porat, E., Richardson, E., Shiran, G., Chachy, I., Chetboun, J., Finkelson, M., Kupchick, M., Zabari, N., Guetta, N., Kotler, N., Bibi, O., Gordon, O., Pan...

    2026 arXiv

  12. [20]

    ACM TOG23(3), 239–248 (2004)

    Hersch, R.D., Chosson, S.: Band moiré images. ACM TOG23(3), 239–248 (2004)

    2004

  13. [21]

    In: International Conference on Learning Representations (ICLR) (2025)

    Hu, R., Zhang, J., Li, Y., Li, J., Guo, Q., Qiu, H., Zhang, T.: Videoshield: Regu- lating diffusion-based video generation models via watermarking. In: International Conference on Learning Representations (ICLR) (2025)

    2025

  14. [22]

    In: Advances in Neural Information Processing Systems

    Internò, C., Geirhos, R., Olhofer, M., Liu, S., Hammer, B., Klindt, D.: Ai-generated video detection via perceptual straightening. In: Advances in Neural Information Processing Systems. vol. 38 (2025)

    2025

  15. [23]

    Wiley (1990)

    Kafri, O., Glatt, I.: The Physics of Moiré Metrology. Wiley (1990)

    1990

  16. [24]

    Kuaishou Technology: Kling: A pioneering ai video generation model.https: //kling.kuaishou.com/(2024)

    2024

  17. [25]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

    Kundu, R., Xiong, H., Mohanty, V., Balachandran, A., Roy-Chowdhury, A.K.: Towards a universal synthetic video detector: From face or background manipula- tions to fully ai-generated content. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition ...

    2025

  18. [26]

    In: Advances in Neural Information Processing Sys- tems

    Li, Z., Wu, X., Shi, G., Qin, Y., Du, H., Zhou, T., Manocha, D., Boyd-Graber, J.L.: Videohallu: Evaluating and mitigating multi-modal hallucinations on syn- thetic video understanding. In: Advances in Neural Information Processing Sys- tems. vol. 38 (2025)

    2025

  19. [27]

    Pattern Recognition 141, 109628 (2023)

    Liu, K., Perov, I., Gao, D., Chervoniy, N., Zhou, W., Zhang, W.: Deepfacelab: Integrated, flexible and extensible face-swapping framework. Pattern Recognition 141, 109628 (2023)

    2023

  20. [28]

    Luma AI: Dream machine: High quality, realistic video generation from text and images.https://lumalabs.ai/dream-machine(2024)

    2024

  21. [29]

    ACM Transactions on Graphics44(5) (2025)

    Michael, P.F., Hao, Z., Belongie, S., Davis, A.: Noise-coded illumination for forensic and photometric video analysis. ACM Transactions on Graphics44(5) (2025). https://doi.org/10.1145/3742892

    2025 doi

  22. [30]

    In: AAAI (2026)

    Ni, Z., Yan, Q., Huang, M., Yuan, T., Tang, Y., Hu, H., Chen, X., Wang, Y.: GenVidBench: A 6-million benchmark for AI-generated video detection. In: AAAI (2026)

    2026

  23. [31]

    IEEE Journal on Selected Areas in Communications42(10), 2642–2658 (2024).https: //doi.org/10.1109/JSAC.2024.3414619

    Ning, J., Xie, L., Li, Y., Chen, Y., Bu, Y., Wang, C., Lu, S., Ye, B.: Moirétracker: Continuous camera-to-screen 6-dof pose tracking based on moiré pattern. IEEE Journal on Selected Areas in Communications42(10), 2642–2658 (2024).https: //doi.org/10.1109/JSAC.2024.3414619

    2024 doi

  24. [32]

    In: Proceedings of the 30th Annual Interna- tional Conference on Mobile Computing and Networking

    Ning, J., Xie, L., Yan, Z., Bu, Y., Luo, J.: Moirévision: A generalized moiré-based mechanism for 6-dof motion sensing. In: Proceedings of the 30th Annual Interna- tional Conference on Mobile Computing and Networking. p. 467–481. ACM Mo- biCom ’24, Association for Computing Ma...

    2024 doi

  25. [33]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)

    Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Niessner, M.: Face- forensics++: Learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)

    2019

  26. [34]

    Runway Research (2025)

    Runway: Introducing Runway Gen-4.5. Runway Research (2025)

    2025

  27. [35]

    In: Proceedings of the 2025 ACM SIGSAC Con- ference on Computer and Communications Security (CCS)

    Schwartz, H., Yan, X., Carver, C.J., Zhou, X.: Combating falsification of speech videos with live optical signatures. In: Proceedings of the 2025 ACM SIGSAC Con- ference on Computer and Communications Security (CCS). pp. 3296–3310 (2025). https://doi.org/10.1145/3719027.3765112

    2025 doi

  28. [36]

    Experimental Mechanics 22(11), 418–433 (1982)

    Sciammarella, C.A.: The moiré method – a review. Experimental Mechanics 22(11), 418–433 (1982)

    1982

  29. [37]

    In: Proceedings of the ACM Symposium on User Interface Software and Technology (UIST) (2025)

    Sethapakdi, T., Perroni-Scharf, M., Li, M., Li, J., Solomon, J., Satyanarayan, A., Mueller, S.: FabObscura: Computational design and fabrication for interactive barrier-grid animations. In: Proceedings of the ACM Symposium on User Interface Software and Technology (UIST) (2025)

    2025

  30. [38]

    Computer Vision, Graphics, and Image Processing30(1), 32–46 (1985)

    Suzuki, S., Abe, K.: Topological structural analysis of digitized binary images by border following. Computer Vision, Graphics, and Image Processing30(1), 32–46 (1985)

    1985

  31. [39]

    Applied Optics9(6), 1467–1472 (1970)

    Takasaki, H.: Moiré topography. Applied Optics9(6), 1467–1472 (1970)

    1970

  32. [40]

    arXiv preprint arXiv:2510.10231 (2025)

    Tan, C., Ming, X., Wang, J., Tao, R., Li, B., Wei, Y., Zhao, Y., Lu, Y.: Semantic visual anomaly detection and reasoning in ai-generated images. arXiv preprint arXiv:2510.10231 (2025)

    2025

  33. [41]

    In: CVPR (2020)

    Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A.: CNN-generated images are surprisingly easy to spot...for now. In: CVPR (2020)

    2020

  34. [42]

    Qing et al

    xAI: Grok imagine video (2025),https://x.ai 18 Y. Qing et al

    2025

  35. [43]

    In: The 34th Annual ACM Symposium on User Interface Software and Technology

    Xiao, C., Zheng, C.: Moiréboard: A stable, accurate and low-cost camera tracking method. In: The 34th Annual ACM Symposium on User Interface Software and Technology. pp. 881–893 (2021)

    2021

  36. [44]

    In: Advances in Neural Information Processing Systems

    Zhang, F., Li, D., Zhang, Q., Chen, J., Liu, G., Lin, J., Yan, J., Liu, J., Zha, Z.J.: Fact-R1: Towards explainable video misinformation detection with deep reasoning. In: Advances in Neural Information Processing Systems. vol. 38 (2025)

    2025

  37. [45]

    arXiv preprint arXiv:1909.01285 (2019)

    Zhang, K.A., Xu, L., Cuesta-Infante, A., Veeramachaneni, K.: Robust invisible video watermarking with attention. arXiv preprint arXiv:1909.01285 (2019)

    1909 arXiv

  38. [46]

    In: Advances in Neural Information Processing Systems

    Zhang, S., Lian, Z., Yang, J., Li, D., Pang, G., Liu, F., Han, B., Li, S., Tan, M.: Physics-driven spatiotemporal modeling for ai-generated video detection. In: Advances in Neural Information Processing Systems. vol. 38 (2025)

    2025

  39. [47]

    IEEE Transactions on pattern analysis and machine intelligence22(11), 1330–1334 (2000)

    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence22(11), 1330–1334 (2000)

    2000

  40. [48]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)

    Zheng, C., Suo, R., Lin, C., Zhao, Z., Yang, L., Liu, S., Yang, M., Wang, C., Shen, C.: D3: Training-free ai-generated video detection using second-order fea- tures. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 12852–12862 (October 2025)

    2025

  41. [49]

    In: European Conference on Computer Vi- sion (ECCV)

    Zou, Z., Gong, B., Wang, L.: Anti-neuron watermarking: Protecting personal data against unauthorized neural networks. In: European Conference on Computer Vi- sion (ECCV). pp. 449–465. Springer (2022)

    2022

  42. [50]

    In: Graphics Gems IV, pp

    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, pp. 474–485. Academic Press (1994) Moiré Video Authentication 19 Supplementary Material This supplementary material provides additional details, experimental results, and discussion to supp...

    1994

  43. [51]

    Place the printed Rear Layer (A4 paper) onto the 3mm Base Layer

  44. [52]

    Overlay the Front Layer (Lenticular Sheet) ensuring the grating lines of both layers are parallel

  45. [53]

    in-the-wild

    Secure the layers together to minimize the air gap between the two gratings, as this gap is critical for maintaining high-contrast Moiré interference across varying viewing angles and distances. B Detailed Description of the Attack Process To comprehensively evaluate the robus...

Pith tools

Reviewed July 13, 2026 · model on record in the stance chip above.