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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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Journal ArticleDOI
Detecting GAN generated Fake Images using Co-occurrence Matrices.
Lakshmanan Nataraj,Tajuddin Manhar Mohammed,B.S. Manjunath,Shivkumar Chandrasekaran,Arjuna Flenner,Jawadul H. Bappy,Amit K. Roy-Chowdhury +6 more
TL;DR: A novel approach to detect GAN generated fake images using a combination of co-occurrence matrices and deep learning, which achieves more than 99% classification accuracy in both datasets.
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Leveraging Frequency Analysis for Deep Fake Image Recognition
TL;DR: It is demonstrated how the frequency representation can be used to identify deep fake images in an automated way, surpassing state-of-the-art methods.
Journal ArticleDOI
Identification of deep network generated images using disparities in color components
TL;DR: This paper proposes a feature set to capture color image statistics for identifying deep network generated (DNG) images and shows that the DNG images are more distinguishable from real ones in the chrominance components, especially in the residual domain.
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Do GANs leave artificial fingerprints
TL;DR: It is shown that each GAN leaves its specific fingerprint in the images it generates, just like real-world cameras mark acquired images with traces of their photo-response non-uniformity pattern.
Proceedings ArticleDOI
Global Texture Enhancement for Fake Face Detection in the Wild
TL;DR: A new architecture coined as Gram-Net, which leverages global image texture representations for robust fake image detection and generalizes significantly better in detecting fake faces from GAN models not seen in the training phase and can perform decently in detectingfake natural images.