生成模型在创建高质量视频方面的令人瞩目的成就引发了人们对数字完整性和隐私漏洞的担忧。近期打击Deepfakes视频的工作开发了检测器，这些检测器在识别GAN生成样本方面具有很高的准确率。然而，这些检测器在由视频创作工具（例如OpenAI的SORA、Runway Gen-2和Pika等）生成的扩散模型生成视频上的鲁棒性仍未得到探索。本文提出了一种新颖框架，用于检测由多种最先进（SOTA）生成模型（如Stable Video Diffusion）合成的视频。我们发现，用于检测扩散生成图像的SOTA方法在识别扩散生成视频方面缺乏鲁棒性。分析表明，这些检测器应用于域外视频时有效性会下降，主要原因是它们难以追踪帧间的时间特征和动态变化。为应对上述挑战，我们使用SOTA视频创作工具收集了一个新的扩散生成视频基准数据集。我们针对视频帧提取扩散模型中显式知识内的表征，并用CNN + LSTM架构训练检测器。评估表明，我们的框架能够很好地捕捉帧间时间特征，在域内视频上达到93.7%的检测准确率，并将域外视频的准确率最高提升16个点。
英文摘要：
The impressive achievements of generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat Deepfakes videos have developed detectors that are highly accurate at identifying GAN-generated samples. However, the robustness of these detectors on diffusion-generated videos generated from video creation tools (e.g., SORA by OpenAI, Runway Gen-2, and Pika, etc.) is still unexplored. In this paper, we propose a novel framework for detecting videos synthesized from multiple state-of-the-art (SOTA) generative models, such as Stable Video Diffusion. We find that the SOTA methods for detecting diffusion-generated images lack robustness in identifying diffusion-generated videos. Our analysis reveals that the effectiveness of these detectors diminishes when applied to out-of-domain videos, primarily because they struggle to track the temporal features and dynamic variations between frames. To address the above-mentioned challenge, we collect a new benchmark video dataset for diffusion-generated videos using SOTA video creation tools. We extract representation within explicit knowledge from the diffusion model for video frames and train our detector with a CNN + LSTM architecture. The evaluation shows that our framework can well capture the temporal features between frames, achieves 93.7% detection accuracy for in-domain videos, and improves the accuracy of out-domain videos by up to 16 points.
Consolidating Diffusion-Generated Video Detection with Unified Multimodal Forgery Learning
School of Computer Science, Shanghai Jiao Tong University（上海交通大学计算机科学学院）；School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与电子工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Department of Computer Science and Engineering, Michigan State University（密歇根州立大学计算机科学与工程系）
TRACE: Trajectory Representation and Consistency Estimation for AI-Generated Video Detection
Zhejiang University（浙江大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Zhejiang Gongshang University（浙江工商大学）
Simple Visual Artifact Detection in Sora-Generated Videos
Joshigakuin Senior High School（女子学院高级中学）；National Institute of Advanced Industrial Science and Technology (AIST)（国立先进工业科学技术研究院）；Visual Geometry Group, University of Oxford（牛津大学视觉几何组）
T2VEval: Benchmark Dataset and Objective Evaluation Method for T2V-generated Videos
School of Information and Communication Engineering, Communication University of China, No.1 East Street, Dingfuzhuang, Chaoyang District, Beijing, China（信息与通信工程学院，中国传媒大学）；Beijing Academy of Artificial Intelligence, No.150 Chengfu Road, Haidian District, Beijing, China（北京人工智能研究院）
VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning
MAIS, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所MAIS部）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences（中国科学院深圳先进技术研究所）
Retrieval-Driven Training-Free AI-Generated Video Attribution
School of Cyber Science and Engineering, Southeast University（东南大学网络空间科学与工程学院）；Purple Mountain Laboratories（紫金山实验室）；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）
DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos
Ludwig Maximilian University of Munich（慕尼黑路德维希-马克西米利安大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）；University of Freiburg（弗赖堡大学）；Technical University of Munich（慕尼黑技术大学）；Huawei Heisenberg Research Center (Munich)（华为海森堡研究中心（慕尼黑））
Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection
School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；Purple Mountain Laboratories（紫金山实验室）；Engineering Research Center of Blockchain Application, Supervision And Management (Southeast University), Ministry of Education（教育部区块链应用监管工程研究中心（东南大学））；School of Computer Science and Engineering, Southeast University（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education（教育部新一代人工智能技术及其跨学科应用重点实验室（东南大学））
Physics-Driven Spatiotemporal Modeling for AI-Generated Video Detection
South China University of Technology（华南理工大学）；University of Science and Technology of China（中国科学技术大学）；Key Laboratory of Big Data and Intelligent Robot, Ministry of Education（教育部大数据与智能机器人重点实验室）；Pazhou Lab（琶洲实验室）；University of Melbourne（墨尔本大学）；Hunan University（湖南大学）；Hong Kong Baptist University（香港 Baptist大学）
VGMShield: Mitigating Misuse of Video Generative Models
University of Virginia（弗吉尼亚大学）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））
VideoSEG-O3: A Multi-turn Reinforcement Learning Framework for Reasoning Video Object Segmentation
School of Automation, Southeast University, Nanjing, China；Baidu Inc, Beijing, China；College of Information Science \& Electronic Engineering, Zhejiang University, Hangzhou, China
