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Open AccessProceedings ArticleDOI

Exposing GAN-Generated Faces Using Inconsistent Corneal Specular Highlights

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TLDR
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.
Abstract
Sophisticated generative adversary network (GAN) models are now able to synthesize highly realistic human faces that are difficult to discern from real ones visually. In this work, we show that GAN synthesized faces can be exposed with the inconsistent corneal specular highlights between two eyes. The inconsistency is caused by the lack of physical/physiological constraints in the GAN models. We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes. Qualitative and quantitative evaluations of our method suggest its simplicity and effectiveness in distinguishing GAN synthesized faces.

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Citations
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Journal ArticleDOI

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 318 research papers carefully surveyed is provided in this article .
Journal ArticleDOI

DeepFake Detection for Human Face Images and Videos: A Survey

- 01 Jan 2022 - 
TL;DR: DeepFake as mentioned in this paper is a generative deep learning algorithm that creates or modifies face features in a superrealistic form, in which it is difficult to distinguish between real and fake features.
Book ChapterDOI

Detection of AI-Generated Synthetic Faces

TL;DR: In this article , the authors present the most effective techniques proposed in the literature for the detection of synthetic faces and analyze their rationale, present real-world application scenarios, and compare different approaches in terms of accuracy and generalization ability.
Journal ArticleDOI

Auguring Fake Face Images Using Dual Input Convolution Neural Network

TL;DR: Wang et al. as mentioned in this paper proposed a dual input convolutional neural network (DICNN) model with ten-fold cross validation with an average training accuracy of 99.36 ± 0.62, a test accuracy of 0.08 ±0.64, and a validation accuracy was 99.30 ± 0 .94.
Book ChapterDOI

The Role of IT Background for Metacognitive Accuracy, Confidence and Overestimation of Deep Fake Recognition Skills

TL;DR: In this paper , the authors proposed a method and metric to detect overconfident individuals in regards to deep fake recognition, which flags individuals overestimating their performance and thus posing a previously unconsidered cybersecurity risk.
References
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Journal ArticleDOI

Generative Adversarial Nets

TL;DR: A new framework for estimating generative models via an adversarial process, in which two models are simultaneously train: a generative model G that captures the data distribution and a discriminative model D that estimates the probability that a sample came from the training data rather than G.
Proceedings ArticleDOI

A Style-Based Generator Architecture for Generative Adversarial Networks

TL;DR: This paper proposed an alternative generator architecture for GANs, borrowing from style transfer literature, which leads to an automatically learned, unsupervised separation of high-level attributes (e.g., pose and identity when trained on human faces) and stochastic variation in the generated images.
Journal ArticleDOI

scikit-image: Image processing in Python

TL;DR: The advantages of open source to achieve the goals of the scikit-image library are highlighted, and several real-world image processing applications that use scik it-image are showcased.
Proceedings Article

Progressive Growing of GANs for Improved Quality, Stability, and Variation

TL;DR: Recently, the authors proposed a new training methodology for GANs that grows both the generator and discriminator progressively, starting from a low resolution, and adding new layers that model increasingly fine details as training progresses.
Journal ArticleDOI

Dlib-ml: A Machine Learning Toolkit

TL;DR: dlib-ml contains an extensible linear algebra toolkit with built in BLAS support, and implementations of algorithms for performing inference in Bayesian networks and kernel-based methods for classification, regression, clustering, anomaly detection, and feature ranking.
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