Stop My Dancing! Understanding, Detecting and Attributing Motion-Aware Deepfake Videos

arXiv:2610.11496 · cs.CV, cs.CR · Submitted 2026-10-08 · Read on arXiv

cs.CV, cs.CR

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Accuracy — 30× in this paper · explained in 1 episode
• Diffusion Model — 24× in this paper · explained in 8 episodes
• Robustness — 20× in this paper · explained in 5 episodes
• Benchmark — 19× in this paper · explained in 2 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Alignment — 13× in this paper · explained in 1 episode
• Aggregation — 11× in this paper · explained in 1 episode
• Motion-Aware Deepfake — 11× in this paper
• AUC — 10× in this paper · explained in 2 episodes
• Consistency — 9× in this paper · explained in 3 episodes
• Conference on Computer Vision and Pattern Recognition — 8× in this paper
• Generalization — 8× in this paper · explained in 13 episodes
• Ablation — 7× in this paper · explained in 1 episode
• Adversarial Attacks — 6× in this paper · explained in 1 episode
• Temporal consistency — 6× in this paper · explained in 2 episodes
• Generative Model — 5× in this paper · explained in 2 episodes
• Learnable High-Pass Filter — 5× in this paper
• Transferability — 5× in this paper · explained in 4 episodes
• Adversarial Training — 4× in this paper · explained in 1 episode
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Generative Models — 4× in this paper · explained in 2 episodes
• Trace — 4× in this paper · explained in 2 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Ablation study — 2× in this paper · explained in 1 episode
• Adaptation — 2× in this paper · explained in 1 episode
• Adversarial Examples — 2× in this paper · explained in 2 episodes
• Attention Mechanism — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Generative Adversarial Network — 2× in this paper
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Zero-Shot Transfer — 2× in this paper · explained in 4 episodes
• Adversarial Noise — 1× in this paper · explained in 1 episode
• Aggregation Function — 1× in this paper · explained in 1 episode
• Analyzer — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Discrete Cosine Transform — 1× in this paper
• Distillation — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frequency-Domain Analysis — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Diffusion Model (LDM) — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Projected Gradient Descent — 1× in this paper
• Provenance — 1× in this paper · explained in 3 episodes
• Pseudo-labels — 1× in this paper · explained in 2 episodes
• Reconstruction Error — 1× in this paper · explained in 2 episodes
• Spatial-Spectral Attack — 1× in this paper
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets
• StyleGAN-Human: A Data-Centric Odyssey of Human Generation
• AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning
• Benchmarking Neural Network Robustness to Common Corruptions and Perturbations
• Adam: A Method for Stochastic Optimization
• Regeneration Based Training-free Attribution of Fake Images Generated by Text-to-Image Generative Models
• Towards Deep Learning Models Resistant to Adversarial Attacks
• Deepfake Generation and Detection: A Benchmark and Survey
• Towards the Detection of Diffusion Model Deepfakes
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning
• Wan: Open and Advanced Large-Scale Video Generative Models
• DisCo: Disentangled Control for Realistic Human Dance Generation
• A Sanity Check for AI-generated Image Detection
• MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance
• PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection
• Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance

Related papers

• Loss Knows Best: Detecting Annotation Errors in Videos via Loss Trajectories
• AnchorWeave: World-Consistent Video Generation with Retrieved Local Spatial Memories
• Benchmarking the Robustness of Foundation Models for Mammography under Domain Shift
• MambaX-Net: Dual-Input Mamba-Enhanced Cross-Attention Network for Longitudinal MRI Segmentation
• TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents
• A Survey on Efficient Vision-Language-Action Models