Synthetic Video Detection Benchmarks

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14 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Temporal Slice Learning for AI-Generated Video Detection with 400 $\times$ Fewer FLOPs
Oct 26, 2026Wei Shang, Zhiyuan Li, Shengfeng HeDeepfake Detection Synthetic Video Detection Benchmarks
School of Computing and Information Systems, Singapore Management University
Verus-Insight: Towards Explainable Artifact Detection in AI-Generated Videos
Oct 22, 2026Zhuoran Zhang, Yang Shi, Qixun Wang +11Synthetic Video Detection Benchmarks Explainable Deepfake Detection
Peking University · Kling Team · HKUST (GZ) +2
DF26: We Cannot Tell Fake From Real Anymore
Sep 7, 2026Severyn Shykula, Andrii Yermakov, Ivan Samarskyi +3T2V Generation OOD Generalization
Ukrainian Catholic University · Faculty of Electrical Engineering, Czech Technical University in Prague · Hover Inc., USA +1
Can We Defend Against AI-Generated Video Attacks on Real-World Crisis Events? A Systematic Evaluation of Detectors, Generators and Social Dissemination
Aug 14, 2026Shuo Liang, Yixing Ma, Pengfei Zhou +33Deepfake Detection Synthetic Video Detection Benchmarks
National University of Singapore · Xidian University · University of California, Berkeley +16
SphereVideo: Prototype-anchored Hyperspherical Boundary for Continual AI-generated Video Detection
Aug 2, 2026Fei Li, Yue Yu, Yuran Wang +3Prototype-Based Learning Deepfake Detection
Fudan University Shanghai, China · Peking University Beijing, China
HumanForge: A Human-Centric Deepfake Video Benchmark with Multi-Agent Forgery Rationales
Jul 9, 2026Wenbo Xu, Zhimin Chen, Xiaojie Liang +2Deepfake Detection Synthetic Video Detection Benchmarks
School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou 510006, China
SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection
Jul 3, 2026Wenlin Wu, Sheng Zhou, Peipei Song +3Deepfake Detection Multi-Agent Reasoning
University of Science and Technology of China · King Abdullah University of Science and Technology · Vast Intelligence Lab
Auditing Generalization in AI-Generated Video Detection: A Six-Control Protocol and the VidAudit Toolkit
Jun 30, 2026Mert Onur Cakiroglu, Zhihe Lu, Mehmet Dalkilic +1Benchmark Auditing OOD Generalization
Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington · College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
CoCoVideo: The High-Quality Commercial-Model-Based Contrastive Benchmark for AI-Generated Video Detection
May 26, 2026Huidong Feng, Wentao Chen, Jie Chen +5Deepfake Detection Synthetic Video Detection Benchmarks
School of Informatics, Xiamen University · China Academy of Information and Communications Technology · AI Transcend Pte. Ltd.
Advancing Reliable Synthetic Video Detection: Insights from the SAFE Challenge
May 7, 2026Kirill Trapeznikov, Gabriel Mancino-Ball, Jonathan Li +9Deepfake Detection Synthetic Video Detection Benchmarks
STR · Drexel University · Aptima, Inc. +1
Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale
Apr 6, 2026Zhengcen Li, Chenyang Jiang, Hang Zhao +7Synthetic Video Detection Benchmarks Deepfake Detection
Harbin Institute of Technology, Shenzhen · Peng Cheng Laboratory · Peking University
GenVideoLens: Where LVLMs Fall Short in AI-Generated Video Detection?
Mar 19, 2026Yueying Zou, Pei Pei Li, Zekun Li +4Synthetic Video Detection Benchmarks Deepfake Detection
Beijing University of Posts and Telecommunications, Beijing, China · University of California, Santa Barbara, CA, USA · Center for Research on Intelligent Perception and Computing, NLPR, Institute of Automation, Chinese Academy of Sciences, Beijing, China
EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection
Feb 19, 2026Hung Mai, Loi Dinh, Duc Hai Nguyen +6Spatio-Temporal Attention Deepfake Detection
N2TP Technology Solution JSC, Hanoi, Vietnam · College of Technology, National Economics University, Hanoi, Vietnam · University of Science, Vietnam National University, Ho Chi Minh City, Vietnam +3
Pistachio: Towards Synthetic, Balanced, and Long-Form Video Anomaly Benchmarks
Nov 22, 2025Jie Li, Hongyi Cai, Mingkang Dong +4Synthetic Benchmark Generation Video Anomaly Detection
Shanghai Jiao Tong University · University of Science & Technology Beijing · Universiti Malaya +2

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

Wei Shang, Zhiyuan Li, Shengfeng He

School of Computing and Information Systems, Singapore Management University

Deepfake Detection Synthetic Video Detection Benchmarks

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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.