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Detection, Attribution and Localization of GAN Generated Images
Michael Goebel,Lakshmanan Nataraj,Tejaswi Nanjundaswamy,Tajuddin Manhar Mohammed,Shivkumar Chandrasekaran,B.S. Manjunath +5 more
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TLDR
A novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods is proposed.Abstract:
Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers. GANs are used in a wide range of tasks, from modifying small attributes of an image (StarGAN [14]), transferring attributes between image pairs (CycleGAN [91]), as well as generating entirely new images (ProGAN [36], StyleGAN [37], SPADE/GauGAN [64]). In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods. For every image, co-occurrence matrices are computed on neighborhood pixels of RGB channels in different directions (horizontal, vertical and diagonal). A deep learning network is then trained on these features to detect, attribute and localize these GAN generated/manipulated images. A large scale evaluation of our approach on 5 GAN datasets comprising over 2.76 million images (ProGAN, StarGAN, CycleGAN, StyleGAN and SPADE/GauGAN) shows promising results in detecting GAN generated images.read more
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“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
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Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
Shu Hu,Yuezun Li,Siwei Lyu +2 more
TL;DR: This work shows that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes, and describes an automatic method to extract and compare corneals from two eyes.
Proceedings ArticleDOI
Exposing GAN-Generated Faces Using Inconsistent Corneal Specular Highlights
Shu Hu,Yuezun Li,Siwei Lyu +2 more
TL;DR: This paper showed that GAN synthesized faces can be exposed with inconsistent corneal specular highlights between two eyes due to the lack of physical/physiological constraints in the GAN models.
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Countering Malicious DeepFakes: Survey, Battleground, and Horizon.
TL;DR: A comprehensive overview and detailed analysis of the research work on the topic of DeepFake generation, DeepFake detection as well as evasion of deepfake detection, with more than 191 research papers carefully surveyed is provided in this article.
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GAN-generated Faces Detection: A Survey and New Perspectives
TL;DR: This work aims to provide a comprehensive review of recent progress in GAN-face detection, focusing on methods that can detect face images that are generated or synthesized from GAN models.
References
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Proceedings ArticleDOI
Can Forensic Detectors Identify GAN Generated Images
TL;DR: This paper investigates how forensic detectors perform in differentiating between GAN generated images and real images, and considers two kinds of approaches, one is intrusive and the other is non-intrusive, based on whether the GAN architecture is needed for performing detection.
Proceedings Article
Source Generator Attribution via Inversion
Michael Albright,Scott McCloskey +1 more
TL;DR: In this article, the problem of attributing a synthetic image to a specific generator in a white box setting is addressed by inverting the process of generation, which enables the generator to simultaneously determine whether the generator produced the image and recover an input which produces a close match to the synthetic image.
Journal ArticleDOI
Identifying Computer-Generated Portraits: The Importance of Training and Incentives.
TL;DR: It is found that observer performance can be significantly improved with the proper incentives in follow-up experiments that reveal how to improve observer performance.
Posted Content
Manipulated Face Detector: Joint Spatial and Frequency Domain Attention Network
Zehao Chen,Hua Yang +1 more
TL;DR: A novel manipulated face detector, which is based on spatial and frequency domain combination and attention mechanism, and adds attention-based layers to backbone networks, in order to improve its generalization ability.
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On Detecting GANs and Retouching based Synthetic Alterations.
TL;DR: In this article, a supervised deep learning algorithm using Convolutional Neural Networks (CNNs) was proposed to detect synthetically altered images and achieved an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset.