Open AccessProceedings Article
Spectral Distribution Aware Image Generation
Steffen Jung,Margret Keuper +1 more
- 18 May 2021
- Vol. 35, Iss: 2, pp 1734-1742
23
TL;DR: In this article, a spectral discriminator is proposed to generate images according to the frequency distribution of the real data by employing a GAN loss, which works stably with different commonly used GAN losses.
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Abstract: Recent advances in deep generative models for photo-realistic images have led to high quality visual results. Such models learn to generate data from a given training distribution such that generated images can not be easily distinguished from real images by the human eye. Yet, recent work on the detection of such fake images pointed out that they are actually easily distinguishable by artifacts in their frequency spectra. In this paper, we propose to generate images according to the frequency distribution of the real data by employing a spectral discriminator. The proposed discriminator is lightweight, modular and works stably with different commonly used GAN losses. We show that the resulting models can better generate images with realistic frequency spectra, which are thus harder to detect by this cue.
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Citations
•Posted Content
Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward.
TL;DR: In this article, a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation and the methodologies used to detect such manipulations for the detection and generation of both audio and video deepfakes.
214
•Proceedings Article
Focal Frequency Loss for Image Reconstruction and Synthesis
Liming Jiang,Bo Dai,Wayne Wu,Chen Change Loy +3 more
- 01 Jan 2021
TL;DR: In this article, the authors propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize by down-weighting the easy ones.
SpectralDefense: Detecting Adversarial Attacks on CNNs in the Fourier Domain
Paula Harder,Franz-Josef Pfreundt,Margret Keuper,Janis Keuper +3 more
- 18 Jul 2021
TL;DR: In this article, the Fourier domain of input images and feature maps can be used to distinguish benign test samples from adversarial images, and two novel detection methods are proposed to detect adversarial attacks.
52
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis.
Yang He,Ning Yu,Margret Keuper,Mario Fritz +3 more
- 09 Aug 2021
TL;DR: BeyondtheSpectrum as discussed by the authors uses super-resolution, denoising and colorization to re-synthesize testing images and extract visual cues for detection of deep-fakes.
Deepfakes generation and detection: state-of-the-art, open challenges, countermeasures, and way forward
04 Jun 2022
TL;DR: In this article , a comprehensive review and detailed analysis of existing tools and machine learning (ML) based approaches for deepfake generation, and the methodologies used to detect such manipulations in both audio and video are presented.
References
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Mehdi Mirza,Simon Osindero +1 more
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Alec Radford,Luke Metz,Soumith Chintala +2 more
- 01 Jan 2016
TL;DR: Deep convolutional generative adversarial networks (DCGANs) as discussed by the authors learn a hierarchy of representations from object parts to scenes in both the generator and discriminator for unsupervised learning.
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Context Encoders: Feature Learning by Inpainting
Deepak Pathak,Philipp Krähenbühl,Jeff Donahue,Trevor Darrell,Alexei A. Efros +4 more
- 27 Jun 2016
TL;DR: It is found that a context encoder learns a representation that captures not just appearance but also the semantics of visual structures, and can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.
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