Online Detection of AI-Generated Images

• URL: http://arxiv.org/abs/2310.15150v1
• Date: Mon, 23 Oct 2023 17:53:14 GMT
• Title: Online Detection of AI-Generated Images
• Authors: David C. Epstein, Ishan Jain, Oliver Wang, Richard Zhang
• Abstract summary: We study generalization in this setting, training on N models and testing on the next (N+k) We extend this approach to pixel prediction, demonstrating strong performance using automatically-generated inpainted data. In addition, for settings where commercial models are not publicly available for automatic data generation, we evaluate if pixel detectors can be trained solely on whole synthetic images.
• Score: 17.30253784649635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With advancements in AI-generated images coming on a continuous basis, it is increasingly difficult to distinguish traditionally-sourced images (e.g., photos, artwork) from AI-generated ones. Previous detection methods study the generalization from a single generator to another in isolation. However, in reality, new generators are released on a streaming basis. We study generalization in this setting, training on N models and testing on the next (N+k), following the historical release dates of well-known generation methods. Furthermore, images increasingly consist of both real and generated components, for example through image inpainting. Thus, we extend this approach to pixel prediction, demonstrating strong performance using automatically-generated inpainted data. In addition, for settings where commercial models are not publicly available for automatic data generation, we evaluate if pixel detectors can be trained solely on whole synthetic images.

Related papers

• Zero-Shot Detection of AI-Generated Images [54.01282123570917]
  We propose a zero-shot entropy-based detector (ZED) to detect AI-generated images. Inspired by recent works on machine-generated text detection, our idea is to measure how surprising the image under analysis is compared to a model of real images. ZED achieves an average improvement of more than 3% over the SoTA in terms of accuracy.
  arXiv  Detail & Related papers  (2024-09-24T08:46:13Z)
• Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities [88.398085358514]
  Contrastive Deepfake Embeddings (CoDE) is a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities.
  arXiv  Detail & Related papers  (2024-07-29T18:00:10Z)
• 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)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)
• How to Trace Latent Generative Model Generated Images without Artificial Watermark? [88.04880564539836]
  Concerns have arisen regarding potential misuse related to images generated by latent generative models. We propose a latent inversion based method called LatentTracer to trace the generated images of the inspected model. Our experiments show that our method can distinguish the images generated by the inspected model and other images with a high accuracy and efficiency.
  arXiv  Detail & Related papers  (2024-05-22T05:33:47Z)
• The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking [47.08666835021915]
  We present a systematic attempt at understanding and detecting AI-generated images (AI-art) in adversarial scenarios. The dataset, named ARIA, contains over 140K images in five categories: artworks (painting), social media images, news photos, disaster scenes, and anime pictures.
  arXiv  Detail & Related papers  (2024-04-22T21:00:13Z)
• PatchCraft: Exploring Texture Patch for Efficient AI-generated Image Detection [39.820699370876916]
  We propose a novel AI-generated image detector capable of identifying fake images created by a wide range of generative models. A novel Smash&Reconstruction preprocessing is proposed to erase the global semantic information and enhance texture patches. Our approach outperforms state-of-the-art baselines by a significant margin.
  arXiv  Detail & Related papers  (2023-11-21T07:12:40Z)
• Securing Deep Generative Models with Universal Adversarial Signature [69.51685424016055]
  Deep generative models pose threats to society due to their potential misuse. In this paper, we propose to inject a universal adversarial signature into an arbitrary pre-trained generative model. The proposed method is validated on the FFHQ and ImageNet datasets with various state-of-the-art generative models.
  arXiv  Detail & Related papers  (2023-05-25T17:59:01Z)
• Augmentation-Interpolative AutoEncoders for Unsupervised Few-Shot Image Generation [45.380129419065746]
  Augmentation-Interpolative AutoEncoders synthesize realistic images of novel objects from only a few reference images. Our procedure is simple and lightweight, generalizes broadly, and requires no category labels or other supervision during training.
  arXiv  Detail & Related papers  (2020-11-25T21:18:55Z)

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.