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Vulnerability of Face Recognition to Deep Morphing

Related publications (52)

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SciLens: Evaluating the Quality of Scientific News Articles Using Social Media and Scientific Literature Indicators

Karl Aberer, Panagiotis Smeros, Carlos Castillo

This paper describes, develops, and validates SciLens, a method to evaluate the quality of scientific news articles. The starting point for our work are structured methodologies that define a series of quality aspects for manually evaluating news. Based on ...

ASSOC COMPUTING MACHINERY2019

Generating Artificial Data for Private Deep Learning

Boi Faltings, Aleksei Triastcyn

In this paper, we propose generating artificial data that retain statistical properties of real data as the means of providing privacy for the original dataset. We use generative adversarial networks to draw privacy-preserving artificial data samples and d ...

2019

Federated Generative Privacy

Boi Faltings, Aleksei Triastcyn

In this paper, we propose FedGP, a framework for privacy-preserving data release in the federated learning setting. We use generative adversarial networks, generator components of which are trained by FedAvg algorithm, to draw privacy-preserving artificial ...

2019

Forged Authenticity: Governing Deepfake Risks

The manipulation of digital content is not a new phenomenon, but the application of machine learning to the creation of deepfakes has (i) radically improved the quality of output, (ii) slashed the resources required to produce realistic fakes at previously ...

EPFL International Risk Governance Center (IRGC)2019

DeepFakes: a New Threat to Face Recognition? Assessment and Detection

Sébastien Marcel

It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto ...

Idiap2018

Every Smile is Unique: Landmark-Guided Diverse Smile Generation

Pascal Fua, Wei Wang

Each smile is unique: one person surely smiles in different ways (e.g. closing/opening the eyes or mouth). Given one input image of a neutral face, can we generate multiple smile videos with distinctive characteristics? To tackle this one-to-many video gen ...

IEEE2018

Characterizing and Improving Stability in Neural Style Transfer

Alexandre Massoud Alahi, Agrim Gupta

Recent progress in style transfer on images has focused on improving the quality of stylized images and speed of methods. However, real-time methods are highly unstable resulting in visible flickering when applied to videos. In this work we characterize th ...

2017

Face Recognition in Real-world Images

Sabine Süsstrunk, Radhakrishna Achanta, Xavier Philippe Marie Fontaine

Face recognition systems are designed to handle well-aligned images captured under controlled situations. However real-world images present varying orientations, expressions, and illumination conditions. Traditional face recognition algorithms perform poor ...

Ieee2017

Embedding Local Quality Measures in Minutiae-Based Biometric Recognition

Mohammad Hamed Izadi

Degradation in data quality is still a main source of errors in the modern biometric recognition systems. However, the data quality can be embedded in the recognition methods at global and local levels to build more accurate biometric systems. Local qualit ...

EPFL2016

Periocular Biometrics in Mobile Environment

Sébastien Marcel, Tiago De Freitas Pereira

In this work we study periocular biometrics in a challenging scenario: a mobile environment, where person recognition can take place on a mobile device. The proposed technique, that models session variability, is evaluated for the authentication task on th ...

IEEE2015