Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Kongresse
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Veranstaltungskalender Awards MyNewsletter Firmensuche
Kunden Login
Über Einzelzugang informieren
Über Unternehmenszugang informieren
Referenzkunden
Elektrische Antriebe und Energiesysteme 2025 | 26 + 27 März 2025

19. Internationaler MTZ-Fachkongress Zukunftsantriebe

Seien Sie bei der Konferenz dabei! Dort treffen Experten aus der Automobilindustrie, Energieerzeugung und Stromnetzbetrieb auf Vertreter aus Politik und Wissenschaft. Diskutiert werden wichtige Themen der Branche.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Kapitel lesen Erstes Kapitel lesen

2025 | OriginalPaper | Buchkapitel

A Comprehensive Exploration on Detecting Fake Images Generated by Stable Diffusion

verfasst von : Jingyi Chen, Xiaolong Wang, Zhijian He, Xiaojiang Peng

Erschienen in: Pattern Recognition and Computer Vision

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Diffusion models, particularly Stable Diffusion Models (SDMs), have recently emerged as a focal point within the generative artificial intelligence sector, acclaimed for their superior visual fidelity and versatility. Despite their rising prominence, the challenge of detecting SDM-generated images has been somewhat overlooked, sparking concerns over their potential misuse for nefarious purposes. This paper aims to delve into the complexities of differentiating authentic images from those generated by SDMs, offering three significant contributions to the field. Firstly, we introduce a varied synthetic image dataset named SDM-Fakes, which consists of six subsets utilizing txt2img, img2img, and inpainting techniques. Secondly, we develop both CNN-based and Transformer-based detection models to identify artificial images, assessing a range of cutting-edge models. Thirdly, we pioneer the evaluation of these detection models’ generalization capabilities across different schemes. We also explored the impact of unknown perturbations on those detectors. Through comprehensive testing, we demonstrate that while current models are adept at recognizing SDM-generated images, there is a significant need to enhance their ability to generalize cross-scheme tasks, as well as robustness on unknown perturbations.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Learning Fully Parametric Subspace Clustering
Nächstes Kapitel Adaptive Margin Global Classifier for Exemplar-Free Class-Incremental Learning
Literatur
1.
Avrahami, O., Fried, O., Lischinski, D.: Blended latent diffusion. ACM Trans. Graph. (TOG) 42(4), 1–11 (2023)CrossRef
2.
Bird, J.J., Lotfi, A.: Cifake: image classification and explainable identification of AI-generated synthetic images. Proc. IEEE Access (2024)
3.
Coccomini, D.A., Esuli, A., Falchi, F., Gennaro, C., Amato, G.: Detecting images generated by diffusers (2023). arXiv preprint arXiv:​2303.​05275
4.
Corvi, R., Cozzolino, D., Zingarini, G., Poggi, G., Nagano, K., Verdoliva, L.: On the detection of synthetic images generated by diffusion models. In: Proceedings of IEEE ICASSP, pp. 1–5 (2023)
5.
Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: Proceedings of IEEE/CVF CVPR, pp. 5781–5790 (2020)
6.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: Proceedings of IEEE/CVF CVPR, pp. 248–255 (2009)
7.
Dodds, K.: Popular geopolitics and audience dispositions: James bond and the internet movie database (imdb). Trans. Inst. Br. Geogr. 31(2), 116–130 (2006)CrossRef
8.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16 x 16 words: transformers for image recognition at scale (2020). arXiv preprint arXiv:​2010.​11929
9.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proceedings of NeurIPS, vol. 27 (2014)
10.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE/CVF CVPR, pp. 770–778 (2016)
11.
He, Y., Gan, B., Chen, S., Zhou, Y., Yin, G., Song, L., Sheng, L., Shao, J., Liu, Z.: Forgerynet: a versatile benchmark for comprehensive forgery analysis. In: Proceedings of IEEE/CVF CVPR, pp. 4360–4369 (2021)
12.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. In: Proceedings of NeurIPS, vol. 33, pp. 6840–6851 (2020)
13.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation (2017). arXiv preprint arXiv:​1710.​10196
14.
Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. In: Proceedings of NeurIPS, vol. 35, pp. 26565–26577 (2022)
15.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE/CVF, pp. 8110–8119 (2020)
16.
Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. In: Proceedings of NeurIPS, vol. 34, pp. 21696–21707 (2021)
17.
Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.L., Yong, M.G., Lee, J., et al.: Mediapipe: a framework for building perception pipelines (2019). arXiv preprint arXiv:​1906.​08172
18.
Marra, F., Gragnaniello, D., Cozzolino, D., Verdoliva, L.: Detection of gan-generated fake images over social networks. In: Proceedings of IEEE MIPR, pp. 384–389 (2018)
19.
Marra, F., Gragnaniello, D., Verdoliva, L., Poggi, G.: Do gans leave artificial fingerprints? In: Proceedings of IEEE MIPR, pp. 506–511 (2019)
20.
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.Y., Ermon, S.: Sdedit: guided image synthesis and editing with stochastic differential equations (2021). arXiv preprint arXiv:​2108.​01073
21.
Nataraj, L., Mohammed, T.M., Chandrasekaran, S., Flenner, A., Bappy, J.H., Roy-Chowdhury, A.K., Manjunath, B.: Detecting gan generated fake images using co-occurrence matrices (2019). arXiv preprint arXiv:​1903.​06836
22.
Neves, J.C., Tolosana, R., Vera-Rodriguez, R., Lopes, V., Proença, H., Fierrez, J.: Ganprintr: improved fakes and evaluation of the state of the art in face manipulation detection. Proc. IEEE J-STSP 14(5), 1038–1048 (2020)
23.
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., Chen, M.: Hierarchical text-conditional image generation with clip latents (2022). arXiv preprint arXiv:​2204.​06125
24.
Ricker, J., Damm, S., Holz, T., Fischer, A.: Towards the detection of diffusion model deepfakes (2022). arXiv preprint arXiv:​2210.​14571
25.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF, pp. 10684–10695 (2022)
26.
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. In: Proceedings of NeurIPS, vol. 35, pp. 36479–36494 (2022)
27.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE/CVF ICCV, pp. 618–626 (2017)
28.
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning, pp. 2256–2265. PMLR (2015)
29.
Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. In: Proceedings of NeurIPS, vol. 32 (2019)
30.
Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations (2020). arXiv preprint arXiv:​2011.​13456
31.
Wang, S.Y., Wang, O., Owens, A., Zhang, R., Efros, A.A.: Detecting photoshopped faces by scripting photoshop. In: Proceedings of IEEE/CVF ICCV, pp. 10072–10081 (2019)
32.
Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A.: CNN-generated images are surprisingly easy to spot... for now. In: Proceedings of IEEE/CVF CVPR, pp. 8695–8704 (2020)
33.
Wang, Z., Bao, J., Zhou, W., Wang, W., Hu, H., Chen, H., Li, H.: Dire for diffusion-generated image detection. In: Proceedings of IEEE/CVF ICCV, pp. 22445–22455 (2023)
34.
Yu, N., Davis, L.S., Fritz, M.: Attributing fake images to gans: Learning and analyzing gan fingerprints. In: Proceedings of IEEE/CVF ICCV, pp. 7556–7566 (2019)
35.
Yuan, L., Chen, Y., Wang, T., Yu, W., Shi, Y., Jiang, Z.H., Tay, F.E., Feng, J., Yan, S.: Tokens-to-token vit: training vision transformers from scratch on imagenet. In: Proceedings of IEEE/CVF ICCV, pp. 558–567 (2021)
36.
Zhang, X., Karaman, S., Chang, S.F.: Detecting and simulating artifacts in gan fake images. In: Proceedings of IEEE WIFS, pp. 1–6 (2019)
37.
Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Learning rich features for image manipulation detection. In: Proceedings of IEEE/CVF CVPR, pp. 1053–1061 (2018)
38.
Zhu, M., Chen, H., Yan, Q., Huang, X., Lin, G., Li, W., Tu, Z., Hu, H., Hu, J., Wang, Y.: Genimage: a million-scale benchmark for detecting AI-generated image. In: Proceedings of NeurIPS, vol. 36 (2024)
Metadaten
Titel
A Comprehensive Exploration on Detecting Fake Images Generated by Stable Diffusion
verfasst von
Jingyi Chen
Xiaolong Wang
Zhijian He
Xiaojiang Peng
Copyright-Jahr
2025
Verlag
Springer Nature Singapore
Buch
Pattern Recognition and Computer Vision

Print ISBN: 978-981-9784-86-8

Electronic ISBN: 978-981-9784-87-5

Copyright-Jahr: 2025

DOI
https://doi.org/10.1007/978-981-97-8487-5_32

Premium Partner

    Bildnachweise