Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality
- Diffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for theDiffusion models recently have been successfully applied for the visual synthesis of strikingly realistic appearing images. This raises strong concerns about their potential for malicious purposes. In this paper, we propose using the lightweight multi Local Intrinsic Dimensionality (multiLID), which has been originally developed in context of the detection of adversarial examples, for the automatic detection of synthetic images and the identification of the according generator networks. In contrast to many existing detection approaches, which often only work for GAN-generated images, the proposed method provides close to perfect detection results in many realistic use cases. Extensive experiments on known and newly created datasets demonstrate that the proposed multiLID approach exhibits superiority in diffusion detection and model identification.Since the empirical evaluations of recent publications on the detection of generated images are often mainly focused on the "LSUN-Bedroom" dataset, we further establish a comprehensive benchmark for the detection of diffusion-generated images, including samples from several diffusion models with different image sizes.The code for our experiments is provided at https://github.com/deepfake-study/deepfake-multiLID.…
Document Type: | Conference Proceeding |
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Conference Type: | Konferenzartikel |
Zitierlink: | https://opus.hs-offenburg.de/8393 | Bibliografische Angaben |
Title (English): | Detecting Images Generated by Deep Diffusion Models using their Local Intrinsic Dimensionality |
Conference: | IEEE/CVF International Conference on Computer Vision Workshops (02-06 October 2023 : Paris, France) |
Author: | Peter Lorenz, Ricard Durall Lopez, Janis KeuperStaff MemberORCiDGND |
Year of Publication: | 2023 |
Publisher: | IEEE |
First Page: | 448 |
Last Page: | 459 |
Parent Title (English): | Proceedings : 2023 IEEE/CVF International Conference on Computer Vision Workshops : ICCVW 2023 |
ISBN: | 979-8-3503-0744-3 (Elektronisch) |
ISBN: | 979-8-3503-0745-0 (Print on Demand) |
ISSN: | 2473-9944 (Elektronisch) |
ISSN: | 2473-9936 (Print on Demand) |
DOI: | https://doi.org/10.1109/ICCVW60793.2023.00051 |
Language: | English | Inhaltliche Informationen |
Institutes: | Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019) |
Forschung / IMLA - Institute for Machine Learning and Analytics | |
Institutes: | Bibliografie | Formale Angaben |
Relevance: | Konferenzbeitrag: h5-Index > 30 |
Open Access: | Closed |
Licence (German): | Urheberrechtlich geschützt |