# Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection

arXiv: https://arxiv.org/abs/2607.00948 · HTML: https://mlsanity.com/paper/2607.00948/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 41 references that are in the map.
- First posted on arXiv 1 Jul 2026.

## Builds on (most cited first, 27 of 27)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets](https://mlsanity.com/paper/2311.15127/) (2023)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [Expanding Language-Image Pretrained Models for General Video Recognition](https://mlsanity.com/paper/2208.02816/) (2022)
- [Lips Don't Lie: A Generalisable and Robust Approach to Face Forgery Detection](https://mlsanity.com/paper/2012.07657/) (2020)
- [Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models](https://mlsanity.com/paper/2305.13873/) (2023)
- [VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2403.06098/) (2024)
- [TALL: Thumbnail Layout for Deepfake Video Detection](https://mlsanity.com/paper/2307.07494/) (2023)
- [DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmark](https://mlsanity.com/paper/2405.19707/) (2024)
- [A Bias-Free Training Paradigm for More General AI-generated Image Detection](https://mlsanity.com/paper/2412.17671/) (2024)
- [Typology of Risks of Generative Text-to-Image Models](https://mlsanity.com/paper/2307.05543/) (2023)
- [Real-Time Deepfake Detection in the Real-World](https://mlsanity.com/paper/2406.09398/) (2024)
- [Fake or JPEG? Revealing Common Biases in Generated Image Detection Datasets](https://mlsanity.com/paper/2403.17608/) (2024)
- [On Learning Multi-Modal Forgery Representation for Diffusion Generated Video Detection](https://mlsanity.com/paper/2410.23623/) (2024)
- [AI-Generated Video Detection via Perceptual Straightening](https://mlsanity.com/paper/2507.00583/) (2025)
- [D3: Training-Free AI-Generated Video Detection Using Second-Order Features](https://mlsanity.com/paper/2508.00701/) (2025)
- [BusterX++: Towards Unified Cross-Modal AI-Generated Content Detection and Explanation with MLLM](https://mlsanity.com/paper/2507.14632/) (2025)
- [Physics-Driven Spatiotemporal Modeling for AI-Generated Video Detection](https://mlsanity.com/paper/2510.08073/) (2025)
- [Seeing What Matters: Generalizable AI-generated Video Detection with Forensic-Oriented Augmentation](https://mlsanity.com/paper/2506.16802/) (2025)
- [As Good As A Coin Toss: Human detection of AI-generated images, videos, audio, and audiovisual stimuli](https://mlsanity.com/paper/2403.16760/) (2024)
- [Towards a Universal Synthetic Video Detector: From Face or Background Manipulations to Fully AI-Generated Content](https://mlsanity.com/paper/2412.12278/) (2024)
- [Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos](https://mlsanity.com/paper/2406.09601/) (2024)
- [VidGuard-R1: AI-Generated Video Detection and Explanation via Reasoning MLLMs and RL](https://mlsanity.com/paper/2510.02282/) (2025)
- [The Criminality From Face Illusion](https://mlsanity.com/paper/2006.03895/) (2020)
- [Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods](https://mlsanity.com/paper/2603.15026/) (2026)
- [Synthetic Human Memories: AI-Edited Images and Videos Can Implant False Memories and Distort Recollection](https://mlsanity.com/paper/2409.08895/) (2024)

## Built on by (most cited first, 1 of 1)

- [Detect Early, Escalate Rarely: Anytime Detection of AI-Generated Video from the Compressed Bitstream](https://mlsanity.com/paper/2607.19476/) (2026)

## Abstract (arXiv)

The visual quality of AI-generated videos has improved drastically in recent years, making it increasingly difficult for humans to distinguish between real and synthetic media. In this work, we evaluate the robustness and applicability of four state-of-the-art motion-based AI-generated video detectors. We identify significant preprocessing and sampling biases in three of the four methods and demonstrate that they account for a substantial portion of their reported performance. Furthermore, we find that these detectors are highly sensitive to motion patterns specific to their evaluation datasets, where AI-generated videos generally exhibit less inter-frame movement than real videos. We show that for all detectors, performance collapses to near-random levels when evaluated on a dataset that does not contain this motion bias. Additionally, through dataset rebalancing and the application of simple spatial augmentations, we observe severe performance degradation across all evaluated models. In contrast, we find that an existing frequency-based detector maintains strong performance across all evaluated datasets, suggesting that frequency-based approaches may offer a more generalizable path forward for AI-generated video detection. We hope that our work raises awareness towards these vulnerabilities and encourages the development of more representative, unbiased datasets and more robust evaluation protocols.