RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection

• URL: http://arxiv.org/abs/2512.10248v1
• Date: Thu, 11 Dec 2025 03:12:56 GMT
• Title: RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection
• Abstract summary: We present RobustSora, the benchmark designed to assess watermark robustness in AIGC video detection.<n>We construct a dataset of 6,500 videos comprising four types: Authentic-Clean (A-C), Authentic-Spoofed with fake watermarks (A-S), Generated-Watermarked (G-W), and Generated-DeWatermarked (G-DeW)<n>Our benchmark introduces two evaluation tasks: Task-I tests performance on watermark-removed AI videos, while Task-II assesses false alarm rates on authentic videos with fake watermarks.
• Score: 1.9083456939533663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of AI-generated video technologies poses challenges to information integrity. While recent benchmarks advance AIGC video detection, they overlook a critical factor: many state-of-the-art generative models embed digital watermarks in outputs, and detectors may partially rely on these patterns. To evaluate this influence, we present RobustSora, the benchmark designed to assess watermark robustness in AIGC video detection. We systematically construct a dataset of 6,500 videos comprising four types: Authentic-Clean (A-C), Authentic-Spoofed with fake watermarks (A-S), Generated-Watermarked (G-W), and Generated-DeWatermarked (G-DeW). Our benchmark introduces two evaluation tasks: Task-I tests performance on watermark-removed AI videos, while Task-II assesses false alarm rates on authentic videos with fake watermarks. Experiments with ten models spanning specialized AIGC detectors, transformer architectures, and MLLM approaches reveal performance variations of 2-8pp under watermark manipulation. Transformer-based models show consistent moderate dependency (6-8pp), while MLLMs exhibit diverse patterns (2-8pp). These findings indicate partial watermark dependency and highlight the need for watermark-aware training strategies. RobustSora provides essential tools to advance robust AIGC detection research.

Related papers

• On the Robustness of Watermarking for Autoregressive Image Generation [21.402408065930796]
  Autoregressive (AR) image generators demand reliable detection and attribution of their outputs to prevent model collapse.<n>Watermarking techniques, specifically designed for AR models, embed a subtle signal at generation time, enabling downstream verification.<n>We study these schemes and demonstrate their vulnerability to both watermark removal and forgery attacks.
  arXiv  Detail & Related papers  (2026-04-13T16:56:48Z)
• OSI: One-step Inversion Excels in Extracting Diffusion Watermarks [56.210696479553945]
  We propose One-step Inversion (OSI), a significantly faster and more accurate method for extracting Gaussian Shading style watermarks.<n>OSI reformulates watermark extraction as a learnable sign classification problem, which eliminates the need for precise regression of the initial noise.<n>Our OSI substantially outperforms the multi-step diffusion inversion method: it is 20x faster, achieves higher extraction accuracy, and doubles the watermark payload capacity.
  arXiv  Detail & Related papers  (2026-02-10T07:43:16Z)
• WMVLM: Evaluating Diffusion Model Image Watermarking via Vision-Language Models [79.32764976020435]
  Digital watermarking is essential for securing generated images from diffusion models.<n>Previous watermark evaluation methods lack a unified framework for both residual and semantic watermarks.<n>We proposeLM, the first unified and interpretable evaluation framework for diffusion model image watermarking via vision-language models.
  arXiv  Detail & Related papers  (2026-01-29T12:14:32Z)
• SPDMark: Selective Parameter Displacement for Robust Video Watermarking [30.398519705830264]
  This work introduces a novel framework for in-generation video watermarking called SPDMark.<n>Watermarks are embedded into the generated videos by modifying a subset of parameters in the generative model.<n> Evaluations on both text-to-video and image-to-video generation models demonstrate the ability of SPDMark to generate imperceptible watermarks.
  arXiv  Detail & Related papers  (2025-12-12T23:35:13Z)
• TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity [76.98973481600002]
  This paper proposes a Tamper-Aware Generative image WaterMarking method named TAG-WM.<n>The proposed method comprises four key modules: a dual-mark joint sampling (DMJS) algorithm for embedding copyright and localization watermarks into the latent space while preserving generative quality.<n>The experimental results demonstrate that TAG-WM achieves state-of-the-art performance in both tampering robustness and localization capability even under distortion.
  arXiv  Detail & Related papers  (2025-06-30T03:14:07Z)
• VideoMarkBench: Benchmarking Robustness of Video Watermarking [34.184333776307504]
  We introduce VideoMarkBench, the first systematic benchmark designed to evaluate the robustness of video watermarks under watermark removal and forgery attacks.<n>Our study encompasses a unified dataset generated by three state-of-the-art video generative models, across three video styles, incorporating four watermarking methods and seven aggregation strategies used during detection.<n>Our findings reveal significant vulnerabilities in current watermarking approaches and highlight the urgent need for more robust solutions.
  arXiv  Detail & Related papers  (2025-05-27T18:00:03Z)
• Towards Dataset Copyright Evasion Attack against Personalized Text-to-Image Diffusion Models [52.877452505561706]
  We propose the first copyright evasion attack specifically designed to undermine dataset ownership verification (DOV)<n>Our CEAT2I comprises three stages: watermarked sample detection, trigger identification, and efficient watermark mitigation.<n>Our experiments show that our CEAT2I effectively evades DOV mechanisms while preserving model performance.
  arXiv  Detail & Related papers  (2025-05-05T17:51:55Z)
• Detection Limits and Statistical Separability of Tree Ring Watermarks in Rectified Flow-based Text-to-Image Generation Models [0.0]
  Tree-Ring Watermarking is a significant technique for authenticating AI-generated images.<n>We evaluate and compare the detection and separability of watermarks between SD 2.1 and FLUX.1-dev models.
  arXiv  Detail & Related papers  (2025-04-04T18:24:23Z)
• AI-generated Image Detection: Passive or Watermark? [4.936341173296691]
  We develop the first comprehensive benchmark to compare the effectiveness, robustness, and efficiency of passive and watermark-based detectors.<n>We evaluate five passive detectors and four watermark-based detectors against eight types of common perturbations and three types of adversarial perturbations.
  arXiv  Detail & Related papers  (2024-11-20T18:59:58Z)
• No Free Lunch in LLM Watermarking: Trade-offs in Watermarking Design Choices [20.20770405297239]
  We show that common design choices in LLM watermarking schemes make the resulting systems surprisingly susceptible to attack. We propose guidelines and defenses for LLM watermarking in practice.
  arXiv  Detail & Related papers  (2024-02-25T20:24:07Z)
• RAW: A Robust and Agile Plug-and-Play Watermark Framework for AI-Generated Images with Provable Guarantees [33.61946642460661]
  This paper introduces a robust and agile watermark detection framework, dubbed as RAW. We employ a classifier that is jointly trained with the watermark to detect the presence of the watermark. We show that the framework provides provable guarantees regarding the false positive rate for misclassifying a watermarked image.
  arXiv  Detail & Related papers  (2024-01-23T22:00:49Z)
• WAVES: Benchmarking the Robustness of Image Watermarks [67.955140223443]
  WAVES (Watermark Analysis Via Enhanced Stress-testing) is a benchmark for assessing image watermark robustness. We integrate detection and identification tasks and establish a standardized evaluation protocol comprised of a diverse range of stress tests. We envision WAVES as a toolkit for the future development of robust watermarks.
  arXiv  Detail & Related papers  (2024-01-16T18:58:36Z)
• ClearMark: Intuitive and Robust Model Watermarking via Transposed Model Training [50.77001916246691]
  This paper introduces ClearMark, the first DNN watermarking method designed for intuitive human assessment. ClearMark embeds visible watermarks, enabling human decision-making without rigid value thresholds. It shows an 8,544-bit watermark capacity comparable to the strongest existing work.
  arXiv  Detail & Related papers  (2023-10-25T08:16:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.