Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis

• URL: http://arxiv.org/abs/2303.10762v4
• Date: Thu, 11 Jul 2024 07:24:11 GMT
• Title: Deep Image Fingerprint: Towards Low Budget Synthetic Image Detection and Model Lineage Analysis
• Authors: Sergey Sinitsa, Ohad Fried,
• Abstract summary: We develop a new detection method for images that are indistinguishable from real ones. Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models. Our approach achieves comparable performance to state-of-the-art pre-trained detection methods on images generated by Stable Diffusion and Midversa.
• Score: 8.777277201807351
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The generation of high-quality images has become widely accessible and is a rapidly evolving process. As a result, anyone can generate images that are indistinguishable from real ones. This leads to a wide range of applications, including malicious usage with deceptive intentions. Despite advances in detection techniques for generated images, a robust detection method still eludes us. Furthermore, model personalization techniques might affect the detection capabilities of existing methods. In this work, we utilize the architectural properties of convolutional neural networks (CNNs) to develop a new detection method. Our method can detect images from a known generative model and enable us to establish relationships between fine-tuned generative models. We tested the method on images produced by both Generative Adversarial Networks (GANs) and recent large text-to-image models (LTIMs) that rely on Diffusion Models. Our approach outperforms others trained under identical conditions and achieves comparable performance to state-of-the-art pre-trained detection methods on images generated by Stable Diffusion and MidJourney, with significantly fewer required train samples.

Related papers

• 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)
• Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images [13.089550724738436]
  Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields. Their ability to create hyper-realistic images poses significant challenges in distinguishing between real and synthetic content. This work introduces a robust detection framework that integrates image and text features extracted by CLIP model with a Multilayer Perceptron (MLP) classifier.
  arXiv  Detail & Related papers  (2024-04-19T14:30:41Z)
• Active Generation for Image Classification [50.18107721267218]
  We propose to address the efficiency of image generation by focusing on the specific needs and characteristics of the model. With a central tenet of active learning, our method, named ActGen, takes a training-aware approach to image generation.
  arXiv  Detail & Related papers  (2024-03-11T08:45:31Z)
• Detecting Generated Images by Real Images Only [64.12501227493765]
  Existing generated image detection methods detect visual artifacts in generated images or learn discriminative features from both real and generated images by massive training. This paper approaches the generated image detection problem from a new perspective: Start from real images. By finding the commonality of real images and mapping them to a dense subspace in feature space, the goal is that generated images, regardless of their generative model, are then projected outside the subspace.
  arXiv  Detail & Related papers  (2023-11-02T03:09:37Z)
• Generalizable Synthetic Image Detection via Language-guided Contrastive Learning [22.4158195581231]
  malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, raises significant concerns regarding the authenticity of images. We propose a simple yet very effective synthetic image detection method via a language-guided contrastive learning and a new formulation of the detection problem. It is shown that our proposed LanguAge-guided SynThEsis Detection (LASTED) model achieves much improved generalizability to unseen image generation models.
  arXiv  Detail & Related papers  (2023-05-23T08:13:27Z)
• Parents and Children: Distinguishing Multimodal DeepFakes from Natural Images [60.34381768479834]
  Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. We pioneer a systematic study on deepfake detection generated by state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-04-02T10:25:09Z)
• Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data [64.65952078807086]
  Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs) Yet, the dark side of such deepfakes, the malicious use of generated media, raises concerns about visual misinformation. We seek a proactive and sustainable solution on deepfake detection by introducing artificial fingerprints into the models.
  arXiv  Detail & Related papers  (2020-07-16T16:49:55Z)
• DeepFake Detection by Analyzing Convolutional Traces [0.0]
  We focus on the analysis of Deepfakes of human faces with the objective of creating a new detection method. The proposed technique, by means of an Expectation Maximization (EM) algorithm, extracts a set of local features specifically addressed to model the underlying convolutional generative process. Results demonstrated the effectiveness of the technique in distinguishing the different architectures and the corresponding generation process.
  arXiv  Detail & Related papers  (2020-04-22T09:02:55Z)

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.