Demography-based facial retouching detection using subclass supervised sparse autoencoder
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TLDR
In this article, the authors introduce a new multi-demographic retouched faces (MDRF) dataset, which contains images belonging to two genders, male and female, and three ethnicities, Indian, Chinese and Caucasian.Abstract:
Digital retouching of face images is becoming more widespread due to the introduction of software packages that automate the task. Several researchers have introduced algorithms to detect whether a face image is original or retouched. However, previous work on this topic has not considered whether or how accuracy of retouching detection varies with the demography of face images. In this paper, we introduce a new Multi-Demographic Retouched Faces (MDRF) dataset, which contains images belonging to two genders, male and female, and three ethnicities, Indian, Chinese, and Caucasian. Further, retouched images are created using two different retouching software packages. The second major contribution of this research is a novel semi-supervised autoencoder incorporating “sub-class” information to improve classification. The proposed approach outperforms existing state-of-the-art detection algorithms for the task of generalized retouching detection. Experiments conducted with multiple combinations of ethnicities show that accuracy of retouching detection can vary greatly based on the demographics of the training and testing images.read more
Citations
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Journal ArticleDOI
PRNU-based detection of facial retouching
Christian Rathgeb,Angelika Botaljov,Fabian Stockhardt,Sergey Isadskiy,Luca Debiasi,Andreas Uhl,Christoph Busch +6 more
TL;DR: A retouching detection system based on the analysis of photo response non-uniformity (PRNU) that is shown to robustly distinguish between bona fide and retouched images achieving an average detection equal error rate of 13.7% across all retoucheding algorithms.
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Evading Face Recognition via Partial Tampering of Faces
TL;DR: A Partial Face Tampering Detection (PFTD) network is proposed, where facial regions are replaced or morphed to generate tampered samples, which surpasses the performance of the existing baseline deep neural networks for tampered image detection.
Proceedings ArticleDOI
On Detecting GANs and Retouching based Synthetic Alterations
TL;DR: A supervised deep learning algorithm using Convolutional Neural Networks (CNNs) to detect synthetically altered images and yields an accuracy of 99.65% on detecting retouching on the ND-IIITD dataset, which outperforms the previous state of the art.
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A Skip Connection Architecture for Localization of Image Manipulations.
TL;DR: An encoder-decoder based network where representations from early layers in the encoder are fused by skip pooling with representations of the last layer of the decoder to use for manipulation detection and can achieve a significantly better performance than the state-of-the-art methods and baselines.
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Face Authenticity: An Overview of Face Manipulation Generation, Detection and Recognition
TL;DR: An overview of the recent technologies on face manipulation generation, detection, recognition, and databases is presented and potential future research directions and challenges are discussed.
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