mlsanityData to 7 Oct 2026

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

arXiv 2406.08231 ↗First posted 12 Jun 2024cs.CV cs.AIcited by 5

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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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 20 of its 28 references that are in the map. First posted on arXiv 12 Jun 2024.

Builds on

20 of 28 references.

References · 20

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8 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 3.

5 papers.

  1. 2024PhysGame: Uncovering Physical Commonsense Violations in Gameplay Videoscited by 21
  2. 2026RefGlitch-Bench: A Benchmark for Reference-based Gameplay Glitch Detection with Vision-Language Modelscited by 3
  3. 2025A Hybrid Co-Finetuning Approach for Visual Bug Detection in Video Gamescited by 2
  4. 2026Order from Chaos: Physical World Understanding from Glitchy Gameplay Videoscited by 1
  5. 2026How Far Can VLMs Go for Visual Bug Detection? Studying 19,738 Keyframes from 41 Hours of Gameplay Videoscited by 1