Detecting GAN-generated Imagery using Color Cues
arXiv:1812.08247
Abstract
Image forensics is an increasingly relevant problem, as it can potentially address online disinformation campaigns and mitigate problematic aspects of social media. Of particular interest, given its recent successes, is the detection of imagery produced by Generative Adversarial Networks (GANs), e.g. `deepfakes'. Leveraging large training sets and extensive computing resources, recent work has shown that GANs can be trained to generate synthetic imagery which is (in some ways) indistinguishable from real imagery. We analyze the structure of the generating network of a popular GAN implementation, and show that the network's treatment of color is markedly different from a real camera in two ways. We further show that these two cues can be used to distinguish GAN-generated imagery from camera imagery, demonstrating effective discrimination between GAN imagery and real camera images used to train the GAN.
References in corpus (1)
Cited by in corpus (7)
- Unmasking DeepFakes with simple Features
- DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
- Exposing GAN-synthesized Faces Using Landmark Locations
- Detecting Deepfake Videos: An Analysis of Three Techniques
- Detection, Attribution and Localization of GAN Generated Images
- Scalable Fine-grained Generated Image Classification Based on Deep Metric Learning
- A Method for Identifying Origin of Digital Images Using a Convolution Neural Network