# Using Deep Convolutional Neural Networks to Detect Rendered Glitches in Video Games

arXiv: https://arxiv.org/abs/2406.08231 · HTML: https://mlsanity.com/paper/2406.08231/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Real-world Anomaly Detection in Surveillance Videos](https://mlsanity.com/paper/1801.04264/) (2018)
- [Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery](https://mlsanity.com/paper/1703.05921/) (2017)
- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [What makes ImageNet good for transfer learning?](https://mlsanity.com/paper/1608.08614/) (2016)
- [A Review on Deep Learning Techniques Applied to Semantic Segmentation](https://mlsanity.com/paper/1704.06857/) (2017)
- [Efficient GAN-Based Anomaly Detection](https://mlsanity.com/paper/1802.06222/) (2018)
- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [Softmax-based Classification is k-means Clustering: Formal Proof, Consequences for Adversarial Attacks, and Improvement through Centroid Based Tailoring](https://mlsanity.com/paper/2001.01987/) (2020)

## Built on by (most cited first, 5 of 5)

- [PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videos](https://mlsanity.com/paper/2412.01800/) (2024)
- [RefGlitch-Bench: A Benchmark for Reference-based Gameplay Glitch Detection with Vision-Language Models](https://mlsanity.com/paper/2604.11082/) (2026)
- [A Hybrid Co-Finetuning Approach for Visual Bug Detection in Video Games](https://mlsanity.com/paper/2510.03591/) (2025)
- [Order from Chaos: Physical World Understanding from Glitchy Gameplay Videos](https://mlsanity.com/paper/2601.16471/) (2026)
- [How Far Can VLMs Go for Visual Bug Detection? Studying 19,738 Keyframes from 41 Hours of Gameplay Videos](https://mlsanity.com/paper/2603.22706/) (2026)

## Abstract (arXiv)

In this paper, we present a method using Deep Convolutional Neural Networks (DCNNs) to detect common glitches in video games. The problem setting consists of an image (800x800 RGB) as input to be classified into one of five defined classes, normal image, or one of four different kinds of glitches (stretched, low resolution, missing and placeholder textures). Using a supervised approach, we train a ShuffleNetV2 using generated data. This work focuses on detecting texture graphical anomalies achieving arguably good performance with an accuracy of 86.8%, detecting 88% of the glitches with a false positive rate of 8.7%, and with the models being able to generalize and detect glitches even in unseen objects. We apply a confidence measure as well to tackle the issue with false positives as well as an effective way of aggregating images to achieve better detection in production. The main use of this work is the partial automatization of graphical testing in the final stages of video game development.