pith. machine review for the scientific record. sign in

arxiv: 2604.26519 · v1 · submitted 2026-04-29 · 💻 cs.CV

Recognition: unknown

GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking

Shupeng Che , Zhiqing Guo , Changtao Miao , Dan Ma , Gaobo Yang
Authors on Pith no claims yet

Pith reviewed 2026-05-07 11:50 UTC · model grok-4.3

classification 💻 cs.CV
keywords deepfake forensicsGIF watermarkingspatiotemporal embeddingproactive authenticationfacial GIFs3D convolutionrobustnessbenchmark dataset
0
0 comments X

The pith

GIFGuard embeds watermarks in facial GIFs using 3D convolutions that remain detectable even after deepfake alterations.

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

The paper introduces GIFGuard as the first watermarking framework built specifically for animated GIFs to address deepfake threats that existing static-image methods cannot handle. It embeds signals with a 3D convolutional encoder that captures motion and temporal dependencies across frames, then extracts them via an attention-equipped decoder that restores features altered by manipulation. The work also releases a new dataset of facial GIFs to support evaluation. This approach aims to let users verify whether short animated clips have been tampered with at a semantic level. If the embedding and extraction hold up, it offers a proactive way to secure temporal media shared on networks rather than relying solely on post-creation detection.

Core claim

GIFGuard is the first spatiotemporal watermarking framework tailored for proactive forensics against deepfakes in facial GIFs. It uses the Spatiotemporal Adaptive Residual Encoder (STARE) with a 3D convolutional backbone and adaptive channel recalibration to embed watermarks that capture globally coherent temporal dependencies, and the Deep Integrity Restoration Decoder (DIRD) with a spatiotemporal hourglass architecture and 3D attention to restore latent features for accurate watermark extraction even under severe facial manipulation. The authors also construct the GIFfaces benchmark dataset to enable systematic research in this area, with results indicating high visual fidelity and strong

What carries the argument

The Spatiotemporal Adaptive Residual Encoder (STARE) with 3D convolutions and adaptive channel recalibration for embedding, paired with the Deep Integrity Restoration Decoder (DIRD) using a spatiotemporal hourglass and 3D attention for extraction under manipulation.

If this is right

  • Watermarked GIFs can be checked for authenticity after potential deepfake processing on social networks.
  • The method supports proactive defense by adding verifiable signals before any tampering occurs.
  • A new benchmark dataset of facial GIFs enables direct comparison of future temporal forensics techniques.
  • Original GIF visual quality stays high while the added watermark provides tamper evidence.
  • Robustness holds across multiple deepfake techniques that target facial content and expressions.

Where Pith is reading between the lines

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

  • The same spatiotemporal embedding strategy could extend to other short-form video formats beyond GIFs.
  • Widespread use might encourage platforms to require watermark checks on user-uploaded animated clips.
  • Further tests on non-facial content would clarify how much the approach depends on facial structure.
  • Pairing the watermark with existing verification systems could create layered checks for animated media.

Load-bearing premise

That 3D convolutional networks with adaptive recalibration and attention-based restoration can reliably recover the embedded watermark signal after deepfake models have made major semantic changes to facial features and motion in the GIF.

What would settle it

A test set of watermarked facial GIFs that are then altered by standard deepfake tools, where the decoder either fails to extract any signal or extracts one that does not match the original embedded pattern.

Figures

Figures reproduced from arXiv: 2604.26519 by Changtao Miao, Dan Ma, Gaobo Yang, Shupeng Che, Zhiqing Guo.

Figure 1
Figure 1. Figure 1: Comparison of proactive forensics paradigms. (a) view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the GIFGuard framework. The architecture consists of three key modules: (1) STARE, an encoder that view at source ↗
Figure 3
Figure 3. Figure 3: Visualization of visual imperceptibility and robustness against mixed attacks. Rows 1-3 illustrate the high fidelity of view at source ↗
Figure 4
Figure 4. Figure 4: Ablation study on the efficacy of the learning strat view at source ↗
read the original abstract

The rapid evolution of deepfake technology poses an unprecedented threat to the authenticity of Graphics Interchange Format (GIF) imagery, which serves as a representative of short-loop temporal media in social networks. However, existing proactive forensics works are designed for static images, which limits their applicability to animated GIFs. To bridge this gap, we propose GIFGuard, the first spatiotemporal watermarking framework tailored for deepfake proactive forensics in GIFs. In the embedding stage, we propose the Spatiotemporal Adaptive Residual Encoder (STARE) to ensure robustness against high-level semantic tampering. It employs a 3D convolutional backbone with adaptive channel recalibration to capture globally coherent temporal dependencies. In the extraction stage, we design the Deep Integrity Restoration Decoder (DIRD). It utilizes a spatiotemporal hourglass architecture equipped with 3D attention to restore latent features, allowing for the accurate extraction of watermark signals even under severe facial manipulation. Furthermore, we construct GIFfaces, the first large-scale benchmark dataset curated for GIF proactive forensics to facilitate research in this domain. Extensive results show that GIFGuard achieves high-fidelity visual quality and remarkable robustness performance against deepfakes. Related code and dataset will be released.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces GIFGuard, the first spatiotemporal watermarking framework for proactive deepfake forensics on facial GIFs. It proposes the Spatiotemporal Adaptive Residual Encoder (STARE) that uses a 3D convolutional backbone with adaptive channel recalibration to embed watermarks while capturing temporal dependencies, and the Deep Integrity Restoration Decoder (DIRD) that employs a spatiotemporal hourglass architecture with 3D attention to restore features and extract watermarks under manipulation. The authors also release the GIFfaces benchmark dataset and report high visual quality plus remarkable robustness against deepfakes.

Significance. If the robustness claims hold under rigorous evaluation, the work would be a meaningful contribution by addressing the gap in proactive forensics for short-loop temporal media such as GIFs, which are common on social platforms. The release of the GIFfaces dataset and associated code is a clear strength that could enable reproducible follow-on research.

major comments (2)
  1. [Method (DIRD subsection) / Experiments] The headline robustness claim rests on DIRD (described in the extraction-stage section). The abstract and method description provide no concrete attack models (e.g., specific face-swap or reenactment pipelines), training distributions, or post-manipulation extraction metrics such as bit-error rate or detection accuracy. Without these, it is impossible to assess whether the 3D attention mechanism actually recovers watermark signals after high-level semantic changes that break temporal coherence.
  2. [Experiments / Results] The experimental section asserts 'extensive results' and 'remarkable robustness' but, consistent with the absence of quantitative tables or ablation studies in the visible material, offers no numbers, baselines, or controls that would allow attribution of performance to the spatiotemporal components versus simpler 2D adaptations of existing image watermarkers.
minor comments (2)
  1. [Abstract / Conclusion] The abstract states that 'related code and dataset will be released' but does not specify the license, exact repository location, or reproducibility instructions (e.g., random seeds, exact training hyperparameters).
  2. [Method] Notation for the adaptive channel recalibration block and the 3D attention layers is introduced without an accompanying diagram or equation reference, making the architectural description harder to follow.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed feedback. The comments identify important areas where additional clarity and quantitative support are needed to strengthen the robustness claims. We address each major comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Method (DIRD subsection) / Experiments] The headline robustness claim rests on DIRD (described in the extraction-stage section). The abstract and method description provide no concrete attack models (e.g., specific face-swap or reenactment pipelines), training distributions, or post-manipulation extraction metrics such as bit-error rate or detection accuracy. Without these, it is impossible to assess whether the 3D attention mechanism actually recovers watermark signals after high-level semantic changes that break temporal coherence.

    Authors: We agree that explicit details on the attack models and metrics are necessary for a rigorous evaluation of DIRD. In the revised manuscript, we will expand the method and experiments sections to specify the concrete pipelines used (including FaceSwap, SimSwap, and First-Order Motion Model for reenactment), the training distributions of the deepfake generators, and the post-manipulation extraction metrics (bit-error rate, detection accuracy, and AUC). These additions will directly demonstrate how the 3D attention restores watermark signals under temporal disruptions. revision: yes

  2. Referee: [Experiments / Results] The experimental section asserts 'extensive results' and 'remarkable robustness' but, consistent with the absence of quantitative tables or ablation studies in the visible material, offers no numbers, baselines, or controls that would allow attribution of performance to the spatiotemporal components versus simpler 2D adaptations of existing image watermarkers.

    Authors: We acknowledge that the current presentation of results lacks the detailed tables, numerical values, and ablation studies required for clear attribution. Although the manuscript references extensive experiments, we will revise the experimental section to include quantitative tables reporting PSNR, SSIM, bit-error rates, and detection accuracies, along with baselines (2D adaptations of HiDDeN and StegaStamp) and ablation studies isolating the 3D convolutional backbone, adaptive recalibration, and attention modules. This will enable direct comparison and attribution of gains to the spatiotemporal design. revision: yes

Circularity Check

0 steps flagged

No circularity detected; engineering proposal with no self-referential derivations

full rationale

The paper introduces GIFGuard as an applied neural architecture (STARE encoder with 3D convolutions and DIRD decoder with 3D attention) for watermark embedding and extraction in GIFs. No equations, fitted parameters renamed as predictions, self-citations invoked as uniqueness theorems, or ansatzes smuggled via prior work appear in the abstract or description. The central claims rest on empirical robustness results from the proposed models rather than any reduction to inputs by construction. The framework is self-contained as a design contribution without load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied deep-learning method paper. No mathematical axioms, free parameters, or newly invented physical entities are described in the provided abstract.

pith-pipeline@v0.9.0 · 5522 in / 1103 out tokens · 46951 ms · 2026-05-07T11:50:03.329140+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

48 extracted references · 8 canonical work pages · 1 internal anchor

  1. [1]

    Irene Amerini, Mauro Barni, Sebastiano Battiato, Paolo Bestagini, Giulia Boato, Vittoria Bruni, Roberto Caldelli, Francesco De Natale, Rocco De Nicola, Luca Guarnera, et al. 2025. Deepfake media forensics: Status and future challenges. Journal of Imaging11, 3 (2025), 73

    2025

  2. [2]

    Luan Chen, Chengyou Wang, Xiao Zhou, and Zhiliang Qin. 2024. Robust and compatible video watermarking via spatio-temporal enhancement and multiscale pyramid attention.IEEE Transactions on Circuits and Systems for Video Technology (2024)

    2024

  3. [3]

    Renwang Chen, Xuanhong Chen, Bingbing Ni, and Yanhao Ge. 2020. Simswap: An efficient framework for high fidelity face swapping. InProceedings of the 28th ACM international conference on multimedia. 2003–2011

    2020

  4. [4]

    Zhilin Chen and Jiaohua Qin. 2025. Reversible data hiding in encrypted images based on histogram shifting and prediction error block coding.International Journal of Autonomous and Adaptive Communications Systems18, 1 (2025), 45–66

    2025

  5. [5]

    Yunjey Choi, Youngjung Uh, Jaejun Yoo, and Jung-Woo Ha. 2020. Stargan v2: Diverse image synthesis for multiple domains. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 8188–8197

    2020

  6. [6]

    Brian Dolhansky, Joanna Bitton, Ben Pflaum, Jikuo Lu, Russ Howes, Menglin Wang, and Cristian Canton Ferrer. 2020. The deepfake detection challenge (dfdc) dataset.arXiv preprint arXiv:2006.07397(2020)

    work page internal anchor Pith review arXiv 2020

  7. [7]

    Han Fang, Zhaoyang Jia, Yupeng Qiu, Jiyi Zhang, Weiming Zhang, and Ee-Chien Chang. 2022. De-END: Decoder-driven watermarking network.IEEE Transactions on Multimedia25 (2022), 7571–7581

    2022

  8. [8]

    Pierre Fernandez, Guillaume Couairon, Hervé Jégou, Matthijs Douze, and Teddy Furon. 2023. The stable signature: Rooting watermarks in latent diffusion models. InProceedings of the IEEE/CVF International Conference on Computer Vision. 22466–22477

    2023

  9. [9]

    Zeki Yalniz, and Alexandre Mourachko

    Pierre Fernandez, Hady Elsahar, I. Zeki Yalniz, and Alexandre Mourachko

  10. [10]

    Video Seal: Open and Efficient Video Watermarking.arXiv preprint arXiv:2412.09492(2024)

    work page arXiv 2024

  11. [11]

    Robert W Floyd. 1976. An adaptive algorithm for spatial gray-scale. InProc. Soc. Inf. Disp., Vol. 17. 75–77

    1976

  12. [12]

    Alexander Groshev, Anastasia Maltseva, Daniil Chesakov, Andrey Kuznetsov, and Denis Dimitrov. 2022. GHOST—a new face swap approach for image and video domains.IEEE Access10 (2022), 83452–83462

    2022

  13. [13]

    Ziyuan He, Zhiqing Guo, Liejun Wang, Gaobo Yang, Yunfeng Diao, and Dan Ma

  14. [14]

    doi:10.1109/TCSVT.2025.3628951

    WaveGuard: Robust Deepfake Detection and Source Tracing via Dual-Tree Complex Wavelet and Graph Neural Networks.IEEE Transactions on Circuits and Systems for Video Technology(2025), 1–1. doi:10.1109/TCSVT.2025.3628951

    work page doi:10.1109/tcsvt.2025.3628951 2025

  15. [15]

    Lixin Jia, Haiyang Sun, Zhiqing Guo, Yunfeng Diao, Dan Ma, and Gaobo Yang

  16. [16]

    InProceedings of the AAAI Conference on Artificial Intelligence

    Uncovering and Mitigating Destructive Multi-Embedding Attacks in Deep- fake Proactive Forensics. InProceedings of the AAAI Conference on Artificial Intelligence

  17. [17]

    Zhaoyang Jia, Han Fang, and Weiming Zhang. 2021. Mbrs: Enhancing robustness of dnn-based watermarking by mini-batch of real and simulated jpeg compres- sion. InProceedings of the 29th ACM international conference on multimedia. 41–49

    2021

  18. [18]

    Xingxun Jiang, Yuan Zong, Wenming Zheng, Chuangao Tang, Wanchuang Xia, Cheng Lu, and Jiateng Liu. 2020. DFEW: A Large-Scale Database for Recognizing Dynamic Facial Expressions in the Wild. InProceedings of the 28th ACM International Conference on Multimedia(Seattle, WA, USA)(MM ’20). Association for Computing Machinery, New York, NY, USA, 2881–2889. doi:1...

    work page doi:10.1145/3394171.3413620 2020

  19. [19]

    Sara Kopelman. 2026. The look of “Aww”: Emotional transcoding in GIF reposito- ries on digital media platforms.new media & society(2026), 14614448251413708

    2026

  20. [20]

    Jiyoung Lee, Seungryong Kim, Sunok Kim, Jungin Park, and Kwanghoon Sohn

  21. [21]

    InProceedings of the IEEE/CVF international conference on computer vision

    Context-aware emotion recognition networks. InProceedings of the IEEE/CVF international conference on computer vision. 10143–10152

  22. [22]

    Yuezun Li, Xin Yang, Pu Sun, Honggang Qi, and Siwei Lyu. 2020. Celeb-df: A large-scale challenging dataset for deepfake forensics. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 3207–3216

    2020

  23. [23]

    Xin Liao, Jing Peng, and Yun Cao. 2021. GIFMarking: The robust watermarking for animated GIF based deep learning.Journal of Visual Communication and Image Representation79 (2021), 103244

    2021

  24. [24]

    Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang. 2015. Deep learning face attributes in the wild. InProceedings of the IEEE international conference on computer vision. 3730–3738

    2015

  25. [25]

    Xiyang Luo, Yinxiao Li, Huiwen Chang, Ce Liu, Peyman Milanfar, and Feng Yang. 2023. Dvmark: a deep multiscale framework for video watermarking.IEEE Transactions on Image Processing(2023)

    2023

  26. [26]

    Souha Mansour, Saoussen Ben Jabra, and Ezzeddine Zagrouba. 2025. A compre- hensive overview of deep learning based video watermarking: Current works, challenges and future trends.Multimedia Tools and Applications84, 24 (2025), 28013–28060

    2025

  27. [27]

    Paarth Neekhara, Shehzeen Hussain, Xinqiao Zhang, Ke Huang, Julian McAuley, and Farinaz Koushanfar. 2024. FaceSigns: Semi-fragile Watermarks for Media Authentication.ACM Trans. Multimedia Comput. Commun. Appl.20, 11, Article 337 (Sept. 2024), 21 pages. doi:10.1145/3640466

    work page doi:10.1145/3640466 2024

  28. [28]

    Hong-Hanh Nguyen-Le, Van-Tuan Tran, Thuc Nguyen, and Nhien-An Le-Khac

  29. [29]

    A Survey on Proactive Deepfake Defense: Disruption and Watermarking. Comput. Surveys(2025)

    2025

  30. [30]

    Jia Peng, Jiaohua Qin, Xuyu Xiang, Yun Tan, and Dashan Qing. 2025. Robust watermarking algorithm for screen-shooting images based on pattern complexity JND model.International Journal of Autonomous and Adaptive Communications Systems18, 2 (2025), 139–158

    2025

  31. [31]

    Albert Pumarola, Antonio Agudo, Aleix M Martinez, Alberto Sanfeliu, and Francesc Moreno-Noguer. 2018. Ganimation: Anatomically-aware facial anima- tion from a single image. InProceedings of the European conference on computer vision (ECCV). 818–833

    2018

  32. [32]

    Andreas Rossler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, and Matthias Nießner. 2019. Faceforensics++: Learning to detect manipulated facial images. InProceedings of the IEEE/CVF international conference on computer vision. 1–11

    2019

  33. [33]

    Chen Sun, Haiyang Sun, Zhiqing Guo, Yunfeng Diao, Liejun Wang, Dan Ma, Gaobo Yang, and Keqin Li. 2026. DiffMark: Diffusion-based robust watermark against Deepfakes.Information Fusion127 (2026), 103801. doi:10.1016/j.inffus. 2025.103801

    work page doi:10.1016/j.inffus 2026

  34. [34]

    Run Wang, Felix Juefei-Xu, Meng Luo, Yang Liu, and Lina Wang. 2021. Faketagger: Robust safeguards against deepfake dissemination via provenance tracking. In Proceedings of the 29th ACM international conference on multimedia. 3546–3555

    2021

  35. [35]

    Tianyi Wang, Mengxiao Huang, Harry Cheng, Xiao Zhang, and Zhiqi Shen

  36. [36]

    InProceedings of the 32nd ACM International Conference on Multimedia

    Lampmark: Proactive deepfake detection via training-free landmark per- ceptual watermarks. InProceedings of the 32nd ACM International Conference on Multimedia. 10515–10524

  37. [37]

    Yuxin Wen, John Jain, John Kirchenbauer, Jonas Goldi, Jonas Geiping, and Tom Goldstein. 2023. Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust. InAdvances in Neural Information Processing Systems (NeurIPS)

    2023

  38. [38]

    Junjiang Wu, Liejun Wang, and Zhiqing Guo. 2026. All in One: Unifying Deepfake Detection, Tampering Localization, and Source Tracing with a Robust Landmark- Identity Watermark. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    2026

  39. [39]

    Xiaoshuai Wu, Xin Liao, and Bo Ou. 2023. Sepmark: Deep separable watermark- ing for unified source tracing and deepfake detection. InProceedings of the 31st ACM International Conference on Multimedia. 1190–1201

    2023

  40. [40]

    Xiaoshuai Wu, Xin Liao, Bo Ou, Yuling Liu, and Zheng Qin. 2024. Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics. InProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI- 24, Kate Larson (Ed.). International Joint Conferences on Artificial Intelligence Organization, 6089–6097. doi:10.249...

    work page doi:10.24963/ijcai.2024/673 2024

  41. [41]

    Zhiliang Xu, Zhibin Hong, Changxing Ding, Zhen Zhu, Junyu Han, Jingtuo Liu, and Errui Ding. 2022. Mobilefaceswap: A lightweight framework for video face swapping. InProceedings of the AAAI Conference on Artificial Intelligence. 2973–2981

    2022

  42. [42]

    Zijin Yang, Kai Zeng, Kejiang Chen, Han Fang, Weiming Zhang, and Nenghai Yu

  43. [43]

    InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Gaussian shading: Provable performance-lossless image watermarking for diffusion models. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12162–12171

  44. [44]

    Kevin Alex Zhang, Lei Xu, Alfredo Cuesta-Infante, and Kalyan Veeramacha- neni. 2019. Robust invisible video watermarking with attention.arXiv preprint arXiv:1909.01285(2019)

    work page arXiv 2019

  45. [45]

    Xuanyu Zhang, Runyi Li, Jiwen Yu, Youmin Xu, Weiqi Li, and Jian Zhang. 2024. Editguard: Versatile image watermarking for tamper localization and copyright protection. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 11964–11974

    2024

  46. [46]

    Yulin Zhang, Jiangqun Ni, Wenkang Su, and Xin Liao. 2023. A novel deep video watermarking framework with enhanced robustness to H. 264/AVC compression. InProceedings of the 31st ACM International Conference on Multimedia. 8095– 8104

    2023

  47. [47]

    Hongrui Zheng, Yuezun Li, Liejun Wang, Yunfeng Diao, and Zhiqing Guo. 2026. Boosting Active Defense Persistence: A Two-Stage Defense Framework Com- bining Interruption and Poisoning Against Deepfake.IEEE Transactions on Information Forensics and Security(2026)

    2026

  48. [48]

    Jiren Zhu, Russell Kaplan, Justin Johnson, and Li Fei-Fei. 2018. Hidden: Hiding data with deep networks. InProceedings of the European conference on computer vision (ECCV). 657–672. 9

    2018