This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models

@article{Tinsley2021ThisFD,
  title={This Face Does Not Exist... But It Might Be Yours! Identity Leakage in Generative Models},
  author={Patrick J. Tinsley and Adam Czajka and Patrick J. Flynn},
  journal={2021 IEEE Winter Conference on Applications of Computer Vision (WACV)},
  year={2021},
  pages={1319-1327}
}
Generative adversarial networks (GANs) are able to generate high resolution photo-realistic images of objects that "do not exist." These synthetic images are rather difficult to detect as fake. However, the manner in which these generative models are trained hints at a potential for information leakage from the supplied training data, especially in the context of synthetic faces. This paper presents experiments suggesting that identity information in face images can flow from the training… 

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References

SHOWING 1-10 OF 32 REFERENCES

GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection

TLDR
The results obtained in the empirical evaluation show that additional efforts are required to develop robust facial manipulation detection systems against unseen conditions and spoof techniques, such as the one proposed in this study.

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

TLDR
This paper presents the first publicly available set of Deepfake videos generated from videos of VidTIMIT database, and demonstrates that GAN-generated Deep fake videos are challenging for both face recognition systems and existing detection methods.

Differentially Private Data Generative Models

TLDR
It is demonstrated that both DP-AuGM and DP-VaeGM can be easily integrated with real-world machine learning applications, such as machine learning as a service and federated learning, which are otherwise threatened by the membership inference attack and the GAN-based attack, respectively.

A Style-Based Generator Architecture for Generative Adversarial Networks

TLDR
An alternative generator architecture for generative adversarial networks is proposed, borrowing from style transfer literature, that improves the state-of-the-art in terms of traditional distribution quality metrics, leads to demonstrably better interpolation properties, and also better disentangles the latent factors of variation.

VGGFace2: A Dataset for Recognising Faces across Pose and Age

TLDR
A new large-scale face dataset named VGGFace2 is introduced, which contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject, and the automated and manual filtering stages to ensure a high accuracy for the images of each identity are described.

GANobfuscator: Mitigating Information Leakage Under GAN via Differential Privacy

TLDR
GANobfuscator, a differentially private GAN, which can achieve differential privacy under GANs by adding carefully designed noise to gradients during the learning procedure, is proposed and theoretically proves that GANob obfuscator can provide strict privacy guarantee with differential privacy.

Generative Adversarial Nets

We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model G that captures the data distribution, and a

SphereFace: Deep Hypersphere Embedding for Face Recognition

TLDR
This paper proposes the angular softmax (A-Softmax) loss that enables convolutional neural networks (CNNs) to learn angularly discriminative features in deep face recognition (FR) problem under open-set protocol.

Analyzing and Improving the Image Quality of StyleGAN

TLDR
This work redesigns the generator normalization, revisit progressive growing, and regularize the generator to encourage good conditioning in the mapping from latent codes to images, and thereby redefines the state of the art in unconditional image modeling.