arXiv CS
open access
Stop My Dancing! Understanding, Detecting and Attributing Motion-Aware Deepfake Videos
Pose-guided diffusion models can now synthesize entire human figures in motion, spawning a new class of deepfakes: Motion Aware Deepfake (MAD) that have already reached hundreds of millions of viewers. To better understand this emerging threat, we construct the first MAD-specific benchmark and measu
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Stop My Dancing! Understanding, Detecting and Attributing Motion-Aware Deepfake Videos#1248776
- InceptionGS: Generative Bootstrapping for Large-Scale Gaussian Splatting under Unstructured View Sampling#920647
- Interpretable Deepfake Detection in Videos via Explicit Forensic Features and Temporal Modeling#1188058
- DDL: A Large-Scale Dataset for Deepfake Detection and Localization in Diversified Real-World Scenarios#1248268
- RLDF: A Region-Level Deepfake Dataset and Benchmark for Partial-Face Manipulation Detection#1356774
Record · ID 1242696
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive