Can Forensic Detectors Identify GAN Generated Images?

By Haodong Li, Han Chen, Bin Li, Shunquan Tan. 2018

23 citations recorded by the source corpus.

Generative adversarial network (GAN) has shown its powerful capability in generating photorealistic images. Although the generated images can fool human eyes, it is not clear whether they can evade the detection of forensic detectors, which aim to identify the originality and authenticity of images. In this paper, we investigate how forensic detectors perform in differentiating between GAN generated images and real images. We consider two kinds of approaches, one is intrusive and the other is non-intrusive, based on whether the GAN architecture is needed for performing detection. We have conducted extensive experiments on a celebrity face image dataset to evaluate the effectiveness of different approaches. The results and analyses show that the intrusive approach can detect GAN generated images but with a relatively high false alarm rate. The non-intrusive approach with features extracted from a VGG network is very effective for detecting GAN generated images when the training data is sufficient, but it still faces challenge when the training data and testing data are mismatched.

Subfields: Computer Vision and Pattern Recognition, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Antonio Torralba
5. Chen Change Loy
6. Jun-Yan Zhu
7. Eli Shechtman
8. Trevor Darrell

In Safety, Robustness & Interpretability

1. Amit K. Roy-Chowdhury
2. Timo Aila
3. Tero Karras
4. Samuli Laine
5. Jaakko Lehtinen
6. Luisa Verdoliva

Citations in the corpus

• Analyzing and Improving the Image Quality of StyleGAN (2019, background): “At present, classifier-based methods can quite reliably detect generated images, regardless of their exact origin [24, 39, 35, 45, 36].”
• Detecting GAN generated Fake Images using Co-occurrence Matrices (2019, background): “d on applying deep learning based methods to detect tampered images [30, 31, 32, 33, 34, 9, 35] The detection of GAN images is a new area in image forensics and there are very few papers in this area [36, 37, 38, 39, 40, 41, 42, 43, 44, 45]. Related fields also include detection of computer generated”