# Hacking VMAF and VMAF NEG: vulnerability to different preprocessing methods

arXiv: https://arxiv.org/abs/2107.04510 · HTML: https://mlsanity.com/paper/2107.04510/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 9 Jul 2021.

## Builds on (most cited first, 3 of 3)

- [ProxIQA: A Proxy Approach to Perceptual Optimization of Learned Image Compression](https://mlsanity.com/paper/1910.08845/) (2019)
- [Hacking VMAF with Video Color and Contrast Distortion](https://mlsanity.com/paper/1907.04807/) (2019)
- [Barriers towards no-reference metrics application to compressed video quality analysis: on the example of no-reference metric NIQE](https://mlsanity.com/paper/1907.03842/) (2019)

## Built on by (most cited first, 8 of 8)

- [Comparing the Robustness of Modern No-Reference Image- and Video-Quality Metrics to Adversarial Attacks](https://mlsanity.com/paper/2310.06958/) (2023)
- [VMAF Re-implementation on PyTorch: Some Experimental Results](https://mlsanity.com/paper/2310.15578/) (2023)
- [IOI: Invisible One-Iteration Adversarial Attack on No-Reference Image- and Video-Quality Metrics](https://mlsanity.com/paper/2403.05955/) (2024)
- [Applicability limitations of differentiable full-reference image-quality](https://mlsanity.com/paper/2212.05499/) (2022)
- [A Tri-Dynamic Preprocessing Framework for UGC Video Compression](https://mlsanity.com/paper/2512.16101/) (2025)
- [IQA-Adapter: Exploring Knowledge Transfer from Image Quality Assessment to Diffusion-based Generative Models](https://mlsanity.com/paper/2412.01794/) (2024)
- [Towards properties of adversarial image perturbations](https://mlsanity.com/paper/2503.14111/) (2025)
- [Adaptive High-Frequency Preprocessing for Video Coding](https://mlsanity.com/paper/2508.08849/) (2025)

## Abstract (arXiv)

Video-quality measurement plays a critical role in the development of video-processing applications. In this paper, we show how video preprocessing can artificially increase the popular quality metric VMAF and its tuning-resistant version, VMAF NEG. We propose a pipeline that tunes processing-algorithm parameters to increase VMAF by up to 218.8%. A subjective comparison revealed that for most preprocessing methods, a video's visual quality drops or stays unchanged. We also show that some preprocessing methods can increase VMAF NEG scores by up to 23.6%.