Proceedings ArticleDOI
Detecting GANs and Retouching Based Digital Alterations via DAD-HCNN
Anubhav Jain,Puspita Majumdar,Richa Singh,Mayank Vatsa +3 more
- pp 2870-2879
TLDR
A hierarchical approach termed as DAD-HCNN which performs two-fold task: it differentiates between digitally generated images and digitally retouched images from the original unaltered images, and to increase the explainability of the decision, it also identifies the GAN architecture used to create the image.Abstract:
While image generation and editing technologies such as Generative Adversarial Networks and Photoshop are being used for creative and positive applications, the misuse of these technologies to create negative applications including Deep-nude and fake news is also increasing at a rampant pace. Therefore, detecting digitally created and digitally altered images is of paramount importance. This paper proposes a hierarchical approach termed as DAD-HCNN which performs two-fold task: (i) it differentiates between digitally generated images and digitally retouched images from the original unaltered images, and (ii) to increase the explainability of the decision, it also identifies the GAN architecture used to create the image. The effectiveness of the model is demonstrated on a database generated by combining face images generated from four different GAN architectures along with the retouched images and original images from existing benchmark databases.read more
Citations
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Journal ArticleDOI
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
TL;DR: This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations, with special attention to the latest generation of DeepFakes.
Journal ArticleDOI
MIPGAN—Generating Strong and High Quality Morphing Attacks Using Identity Prior Driven GAN
Haoyu Zhang,Sushma Venkatesh,Raghavendra Ramachandra,Kiran B. Raja,Naser Damer,Christoph Busch +5 more
TL;DR: The proposed MIPGAN is derived from the StyleGAN with a newly formulated loss function exploiting perceptual quality and identity factor to generate a high quality morphed facial image with minimal artefacts and with high resolution.
Journal ArticleDOI
Fighting Deepfake by Exposing the Convolutional Traces on Images
TL;DR: A new approach aimed to extract a Deepfake fingerprint from images is proposed, based on the Expectation-Maximization algorithm trained to detect and extract a fingerprint that represents the Convolutional Traces left by GANs during image generation.
Posted Content
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
TL;DR: In this paper, the authors provide a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations, including entire face synthesis, identity swap (DeepFakes), attribute manipulation and expression swap.
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Detection, Attribution and Localization of GAN Generated Images
Michael Goebel,Lakshmanan Nataraj,Tejaswi Nanjundaswamy,Tajuddin Manhar Mohammed,Shivkumar Chandrasekaran,B.S. Manjunath +5 more
TL;DR: A novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods is proposed.
References
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
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Proceedings ArticleDOI
A conditional random field for automatic photo editing
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TL;DR: A system that is trained to correct red-eye, reduce specularities, and remove acne and other blemishes from faces is demonstrated, showing results with test images scavenged from acne-themed internet message boards.
Posted Content
Detecting Face2Face Facial Reenactment in Videos
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