# Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

arXiv: https://arxiv.org/abs/1804.04418 · HTML: https://mlsanity.com/paper/1804.04418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Apr 2018.

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

- [Current Status and Trends in Image Anti-Forensics Research: A Bibliometric Analysis](https://mlsanity.com/paper/2408.11365/) (2024)

## Abstract (arXiv)

Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security. While most approaches focus on the image rendering phase, this paper presents a method based on increasing the naturalness of CG facial images from the perspective of spoofing detectors. The proposed method is implemented using a convolutional neural network (CNN) comprising two autoencoders and a transformer and is trained using a black-box discriminator without gradient information. Over 50% of the transformed CG images were not detected by three state-of-the-art spoofing detectors. This capability raises an alarm regarding the reliability of facial authentication systems, which are becoming widely used in daily life.