# D3: Training-Free AI-Generated Video Detection Using Second-Order Features

arXiv: https://arxiv.org/abs/2508.00701 · HTML: https://mlsanity.com/paper/2508.00701/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 60 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 30 of 44)

- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [CogVideoX: Text-to-Video Diffusion Models with An Expert Transformer](https://mlsanity.com/paper/2408.06072/) (2024)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [WorldSimBench: Towards Video Generation Models as World Simulators](https://mlsanity.com/paper/2410.18072/) (2024)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [Open-Sora: Democratizing Efficient Video Production for All](https://mlsanity.com/paper/2412.20404/) (2024)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [VideoCrafter2: Overcoming Data Limitations for High-Quality Video Diffusion Models](https://mlsanity.com/paper/2401.09047/) (2024)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [Towards Universal Fake Image Detectors that Generalize Across Generative Models](https://mlsanity.com/paper/2302.10174/) (2023)
- [DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors](https://mlsanity.com/paper/2310.12190/) (2023)
- [DIRE for Diffusion-Generated Image Detection](https://mlsanity.com/paper/2303.09295/) (2023)
- [I2VGen-XL: High-Quality Image-to-Video Synthesis via Cascaded Diffusion Models](https://mlsanity.com/paper/2311.04145/) (2023)
- [EvalCrafter: Benchmarking and Evaluating Large Video Generation Models](https://mlsanity.com/paper/2310.11440/) (2023)
- [Expanding Language-Image Pretrained Models for General Video Recognition](https://mlsanity.com/paper/2208.02816/) (2022)
- [Exposing DeepFake Videos By Detecting Face Warping Artifacts](https://mlsanity.com/paper/1811.00656/) (2018)
- [Show-1: Marrying Pixel and Latent Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2309.15818/) (2023)
- [Exposing Deep Fakes Using Inconsistent Head Poses](https://mlsanity.com/paper/1811.00661/) (2018)
- [On the detection of synthetic images generated by diffusion models](https://mlsanity.com/paper/2211.00680/) (2022)
- [StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text](https://mlsanity.com/paper/2403.14773/) (2024)
- [SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction](https://mlsanity.com/paper/2310.20700/) (2023)
- [VideoPhy: Evaluating Physical Commonsense for Video Generation](https://mlsanity.com/paper/2406.03520/) (2024)
- [DreamVideo: Composing Your Dream Videos with Customized Subject and Motion](https://mlsanity.com/paper/2312.04433/) (2023)
- [Exploring Temporal Coherence for More General Video Face Forgery Detection](https://mlsanity.com/paper/2108.06693/) (2021)
- [VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2403.06098/) (2024)
- [Intriguing properties of synthetic images: from generative adversarial networks to diffusion models](https://mlsanity.com/paper/2304.06408/) (2023)
- [Noiseprint: a CNN-based camera model fingerprint](https://mlsanity.com/paper/1808.08396/) (2018)

## Built on by (most cited first, 30 of 34)

- [VideoVeritas: AI-Generated Video Detection via Perception Pretext Reinforcement Learning](https://mlsanity.com/paper/2602.08828/) (2026)
- [Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods](https://mlsanity.com/paper/2603.15026/) (2026)
- [Face2Parts: Exploring Coarse-to-Fine Inter-Regional Facial Dependencies for Generalized Deepfake Detection](https://mlsanity.com/paper/2603.26036/) (2026)
- [MVAD: A Benchmark Dataset for Multimodal AI-Generated Video-Audio Detection](https://mlsanity.com/paper/2512.00336/) (2025)
- [RobustSora: De-Watermarked Benchmark for Robust AI-Generated Video Detection](https://mlsanity.com/paper/2512.10248/) (2025)
- [CMTA: Leveraging Cross-Modal Temporal Artifacts for Generalizable AI-Generated Video Detection](https://mlsanity.com/paper/2605.00630/) (2026)
- [SPLIT: Training-Free AI-Generated and Partially Edited Video Detection via Spatial Patch-Level Incoherence and Temporal Roughness](https://mlsanity.com/paper/2607.02886/) (2026)
- [SafeGuard: A Multi-Agent Perception-Reasoning Framework for Social-Risk AI-Generated Video Detection](https://mlsanity.com/paper/2607.03069/) (2026)
- [Grab-3D: Detecting AI-Generated Videos from 3D Geometric Temporal Consistency](https://mlsanity.com/paper/2512.13665/) (2025)
- [GenVideoLens: Where LVLMs Fall Short in AI-Generated Video Detection?](https://mlsanity.com/paper/2603.18625/) (2026)
- [SpecSem-Net: Integrating Spectral and Semantic Features for Robust AI-generated Video Detection](https://mlsanity.com/paper/2605.17311/) (2026)
- [Video as Natural Augmentation: Towards Unified AI-Generated Image and Video Detection](https://mlsanity.com/paper/2605.21977/) (2026)
- [ReConFuse: Reconstruction-Error Guided Semantic Fusion for AI-Generated Video Detection](https://mlsanity.com/paper/2606.04706/) (2026)
- [Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection](https://mlsanity.com/paper/2606.16742/) (2026)
- [Auditing Generalization in AI-Generated Video Detection: A Six-Control Protocol and the VidAudit Toolkit](https://mlsanity.com/paper/2606.31004/) (2026)
- [Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection](https://mlsanity.com/paper/2607.00948/) (2026)
- [Detecting AI-Generated Video: A Vision-Language Dual-View Survey](https://mlsanity.com/paper/2607.10787/) (2026)
- [SphereVideo: Prototype-anchored Hyperspherical Boundary for Continual AI-generated Video Detection](https://mlsanity.com/paper/2608.01334/) (2026)
- [EA-Swin: An Embedding-Agnostic Swin Transformer for AI-Generated Video Detection](https://mlsanity.com/paper/2602.17260/) (2026)
- [3DSPA: A 3D Semantic Point Autoencoder for Evaluating Video Realism](https://mlsanity.com/paper/2602.20354/) (2026)
- [Moir'e Video Authentication: A Physical Signature Against AI Video Generation](https://mlsanity.com/paper/2604.01654/) (2026)
- [Detecting AI-Generated Videos with Spiking Neural Networks](https://mlsanity.com/paper/2605.05895/) (2026)
- [Explainable Forensics of Manipulated Segments in Untrimmed Long Videos](https://mlsanity.com/paper/2606.02402/) (2026)
- [What Do Deepfake Benchmarks Measure? An Audit Using Frozen Self-Supervised Representations](https://mlsanity.com/paper/2606.26384/) (2026)
- [G2VD: Generalizable AI-Generated Video Detection via Counterfactual Intervention and Causal Disentanglement](https://mlsanity.com/paper/2607.04607/) (2026)
- [Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream](https://mlsanity.com/paper/2607.19476/) (2026)
- [FakeI2V-Bench: Benchmarking the Applicability of Image-level Deepfake Detectors for Deepfake Video Detection](https://mlsanity.com/paper/2608.03096/) (2026)
- [MotionPhys: Detecting AI-Generated Videos via Physical Consistency of Optical-Flow Trajectories](https://mlsanity.com/paper/2608.20770/) (2026)
- [Mind the Rift: Cross-Scale Coupling Mismatch for AI-Generated Video Detection](https://mlsanity.com/paper/2609.00742/) (2026)
- [PIVOT: Physics-Grounded Verification for AI-Generated Audio-Video Detection](https://mlsanity.com/paper/2609.15562/) (2026)

## Abstract (arXiv)

The evolution of video generation techniques, such as Sora, has made it increasingly easy to produce high-fidelity AI-generated videos, raising public concern over the dissemination of synthetic content. However, existing detection methodologies remain limited by their insufficient exploration of temporal artifacts in synthetic videos. To bridge this gap, we establish a theoretical framework through second-order dynamical analysis under Newtonian mechanics, subsequently extending the Second-order Central Difference features tailored for temporal artifact detection. Building on this theoretical foundation, we reveal a fundamental divergence in second-order feature distributions between real and AI-generated videos. Concretely, we propose Detection by Difference of Differences (D3), a novel training-free detection method that leverages the above second-order temporal discrepancies. We validate the superiority of our D3 on 4 open-source datasets (Gen-Video, VideoPhy, EvalCrafter, VidProM), 40 subsets in total. For example, on GenVideo, D3 outperforms the previous best method by 10.39% (absolute) mean Average Precision. Additional experiments on time cost and post-processing operations demonstrate D3's exceptional computational efficiency and strong robust performance. Our code is available at https://github.com/Zig-HS/D3.