# Identification of Deep Network Generated Images Using Disparities in Color Components

arXiv: https://arxiv.org/abs/1808.07276 · HTML: https://mlsanity.com/paper/1808.07276/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2018.

## Built on by (most cited first, 18 of 18)

- [Exploring Temporal Coherence for More General Video Face Forgery Detection](https://mlsanity.com/paper/2108.06693/) (2021)
- [Are GAN generated images easy to detect? A critical analysis of the state-of-the-art](https://mlsanity.com/paper/2104.02617/) (2021)
- [Improving the Efficiency and Robustness of Deepfakes Detection through Precise Geometric Features](https://mlsanity.com/paper/2104.04480/) (2021)
- [Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces](https://mlsanity.com/paper/2109.00162/) (2021)
- [GAN-generated Faces Detection: A Survey and New Perspectives](https://mlsanity.com/paper/2202.07145/) (2022)
- [Robust Attentive Deep Neural Network for Exposing GAN-generated Faces](https://mlsanity.com/paper/2109.02167/) (2021)
- [Exposing Deepfake Face Forgeries with Guided Residuals](https://mlsanity.com/paper/2205.00753/) (2022)
- [Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces](https://mlsanity.com/paper/2205.06680/) (2022)
- [Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models](https://mlsanity.com/paper/2104.02107/) (2021)
- [Synthetic Image Detection: Highlights from the IEEE Video and Image Processing Cup 2022 Student Competition](https://mlsanity.com/paper/2309.12428/) (2023)
- [Detecting Deepfake Videos Using Euler Video Magnification](https://mlsanity.com/paper/2101.11563/) (2021)
- [Distinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet](https://mlsanity.com/paper/2110.09428/) (2021)
- [Controlled AutoEncoders to Generate Faces from Voices](https://mlsanity.com/paper/2107.07988/) (2021)
- [Few-shot Forgery Detection via Guided Adversarial Interpolation](https://mlsanity.com/paper/2204.05905/) (2022)
- [INSIGHT: An Interpretable Neural Vision-Language Framework for Reasoning of Generative Artifacts](https://mlsanity.com/paper/2511.22351/) (2025)
- [When Handcrafted Features and Deep Features Meet Mismatched Training and Test Sets for Deepfake Detection](https://mlsanity.com/paper/2209.13289/) (2022)
- [Deepfake Detection of Occluded Images Using a Patch-based Approach](https://mlsanity.com/paper/2304.04537/) (2023)
- [Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations](https://mlsanity.com/paper/2312.08034/) (2023)

## Abstract (arXiv)

With the powerful deep network architectures, such as generative adversarial networks, one can easily generate photorealistic images. Although the generated images are not dedicated for fooling human or deceiving biometric authentication systems, research communities and public media have shown great concerns on the security issues caused by these images. This paper addresses the problem of identifying deep network generated (DNG) images. Taking the differences between camera imaging and DNG image generation into considerations, we analyze the disparities between DNG images and real images in different color components. We observe that the DNG images are more distinguishable from real ones in the chrominance components, especially in the residual domain. Based on these observations, we propose a feature set to capture color image statistics for identifying DNG images. Additionally, we evaluate several detection situations, including the training-testing data are matched or mismatched in image sources or generative models and detection with only real images. Extensive experimental results show that the proposed method can accurately identify DNG images and outperforms existing methods when the training and testing data are mismatched. Moreover, when the GAN model is unknown, our methods also achieves good performance with one-class classification by using only real images for training.