Finding AI-Generated Faces in the Wild

• URL: http://arxiv.org/abs/2311.08577v3
• Date: Fri, 5 Apr 2024 17:37:36 GMT
• Title: Finding AI-Generated Faces in the Wild
• Authors: Gonzalo J. Aniano Porcile, Jack Gindi, Shivansh Mundra, James R. Verbus, Hany Farid,
• Abstract summary: We focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected.
• Score: 9.390562437823078
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: AI-based image generation has continued to rapidly improve, producing increasingly more realistic images with fewer obvious visual flaws. AI-generated images are being used to create fake online profiles which in turn are being used for spam, fraud, and disinformation campaigns. As the general problem of detecting any type of manipulated or synthesized content is receiving increasing attention, here we focus on a more narrow task of distinguishing a real face from an AI-generated face. This is particularly applicable when tackling inauthentic online accounts with a fake user profile photo. We show that by focusing on only faces, a more resilient and general-purpose artifact can be detected that allows for the detection of AI-generated faces from a variety of GAN- and diffusion-based synthesis engines, and across image resolutions (as low as 128 x 128 pixels) and qualities.

Related papers

• Analysis of Human Perception in Distinguishing Real and AI-Generated Faces: An Eye-Tracking Based Study [6.661332913985627]
  We investigate how humans perceive and distinguish between real and fake images. Our analysis of StyleGAN-3 generated images reveals that participants can distinguish real from fake faces with an average accuracy of 76.80%.
  arXiv  Detail & Related papers  (2024-09-23T19:34:30Z)
• A Sanity Check for AI-generated Image Detection [49.08585395873425]
  We present a sanity check on whether the task of AI-generated image detection has been solved. To quantify the generalization of existing methods, we evaluate 9 off-the-shelf AI-generated image detectors on Chameleon dataset. We propose AIDE (AI-generated Image DEtector with Hybrid Features), which leverages multiple experts to simultaneously extract visual artifacts and noise patterns.
  arXiv  Detail & Related papers  (2024-06-27T17:59:49Z)
• AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark [12.368133562194267]
  We introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors.
  arXiv  Detail & Related papers  (2024-06-02T15:51:33Z)
• Towards the Detection of AI-Synthesized Human Face Images [12.090322373964124]
  This paper presents a benchmark including human face images produced by Generative Adversarial Networks (GANs) and a variety of DMs. Then, the forgery traces introduced by different generative models have been analyzed in the frequency domain to draw various insights. The paper further demonstrates that a detector trained with frequency representation can generalize well to other unseen generative models.
  arXiv  Detail & Related papers  (2024-02-13T19:37:44Z)
• Seeing is not always believing: Benchmarking Human and Model Perception of AI-Generated Images [66.20578637253831]
  There is a growing concern that the advancement of artificial intelligence (AI) technology may produce fake photos. This study aims to comprehensively evaluate agents for distinguishing state-of-the-art AI-generated visual content.
  arXiv  Detail & Related papers  (2023-04-25T17:51:59Z)
• Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces [51.56417104929796]
  We develop an online platform called Open-eye to study the human performance of AI-synthesized faces detection. We describe the design and workflow of the Open-eye in this paper.
  arXiv  Detail & Related papers  (2022-05-13T14:30:59Z)
• Detecting High-Quality GAN-Generated Face Images using Neural Networks [23.388645531702597]
  We propose a new strategy to differentiate GAN-generated images from authentic images by leveraging spectral band discrepancies. In particular, we enable the digital preservation of face images using the Cross-band co-occurrence matrix and spatial co-occurrence matrix. We show that the performance boost is particularly significant and achieves more than 92% in different post-processing environments.
  arXiv  Detail & Related papers  (2022-03-03T13:53:27Z)
• End2End Occluded Face Recognition by Masking Corrupted Features [82.27588990277192]
  State-of-the-art general face recognition models do not generalize well to occluded face images. This paper presents a novel face recognition method that is robust to occlusions based on a single end-to-end deep neural network. Our approach, named FROM (Face Recognition with Occlusion Masks), learns to discover the corrupted features from the deep convolutional neural networks, and clean them by the dynamically learned masks.
  arXiv  Detail & Related papers  (2021-08-21T09:08:41Z)
• SynFace: Face Recognition with Synthetic Data [83.15838126703719]
  We devise the SynFace with identity mixup (IM) and domain mixup (DM) to mitigate the performance gap. We also perform a systematically empirical analysis on synthetic face images to provide some insights on how to effectively utilize synthetic data for face recognition.
  arXiv  Detail & Related papers  (2021-08-18T03:41:54Z)
• Deepfake Forensics via An Adversarial Game [99.84099103679816]
  We advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. Considering that AI-based face manipulation often leads to high-frequency artifacts that can be easily spotted by models yet difficult to generalize, we propose a new adversarial training method that attempts to blur out these specific artifacts.
  arXiv  Detail & Related papers  (2021-03-25T02:20:08Z)
• Fighting Deepfake by Exposing the Convolutional Traces on Images [0.0]
  Mobile apps like FACEAPP make use of the most advanced Generative Adversarial Networks (GAN) to produce extreme transformations on human face photos. This kind of media object took the name of Deepfake and raised a new challenge in the multimedia forensics field: the Deepfake detection challenge. In this paper, a new approach aimed to extract a Deepfake fingerprint from images is proposed.
  arXiv  Detail & Related papers  (2020-08-07T08:49:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.