Abstract
The proliferation of synthetic media over the internet is posing a significant social threat. Recent advancements in Diffusion Models (DM) have made it easier to create astonishingly photo-realistic synthetic media with high stability and control. Moreover, applications like DALLE-2, powered by DM and Large Language Models (LLM), permit visual content generation from natural language description, enabling opportunities for everyone to generate visual media. Hence, there is an immediate need to identify synthetic images and attribute them to their source architectures. In this work, we propose a synthetic image detector as universal detector and a source model attributor based on a popular transfer-learning model ResNet-50 and compare the results with other popular models, including Visual Geometry Group (VGG) 16, XceptionNet and InceptionNet. The proposed universal detector attains over 96% accuracy, with a source attribution, accuracy over 93% for detection of Diffusion Model generated images. The model also succeeds in achieving significant generalization and robustness capabilities under different training-testing configurations, as proven by our experiments.
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References
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139–144 (2020)
Lago, F., Pasquini, C., Böhme, R., Dumont, H., Goffaux, V., Boato, G.: More real than real: a study on human visual perception of synthetic faces [applications corner]. IEEE Signal Process. Mag. 39(1), 109–116 (2021)
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 Conference on Computer Vision and Pattern Recognition, pp. 8110–8119 (2020)
Hancock, J.T., Bailenson, J.N.: The social impact of DeepFakes. Cyberpsychol. Behav. Soc. Netw. 24(3), 149–152 (2021). PMID: 33760669
Dhariwal, P., Nichol, A.: Diffusion models beat GANs on image synthesis. In: Advances in Neural Information Processing, vol. 34, pp. 8780–8794 (2021)
Nichol, A., Ramesh, A., Dhariwal, P.: Hierarchical text-conditional image generation with clip Latents (2022)
Lorenz, D., Rombach, R., Blattmann, A.: High-resolution image synthesis with latent diffusion models (2022)
Passos, L. A., Jodas, D., da Costa, K. A., Júnior, L. A. S., Colombo, D., Papa, J. P.: A review of deep learning-based approaches for DeepFake content detection. arXiv preprint arXiv:2202.06095 (2022)
Cozzolino, D., Gragnaniello, D., Poggi, G., Verdoliva, L.: Towards universal GAN image detection. In: 2021 International Conference on Visual Communications and Image Processing (VCIP), pp. 1–5. IEEE (2021)
Zingarini, G., Corvi, R., Cozzolino, D.: On the detection of synthetic images generated by diffusion models (2022)
Yu, N., Sha, Z., Li, Z.: De-fake: detection and attribution of fake images generated by text-to-image generation models (2023)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10684–10695 (2022)
Ramesh, A., Nichol, A., Dhariwal, P.: GLIDE: towards photorealistic image generation and editing with text-guided diffusion models (2022)
Dayma, B.: Dall\(\cdot \)e mini, vol. 7 (2021)
Hodosh, M., Young, P., Hockenmaier, J.: Framing image description as a ranking task: data, models and evaluation metrics. J. Artif. Intell. Res. 47, 853–899 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1251–1258 (2017)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255. IEEE (2009)
Towsley, D., Atwood, J.: Diffusion-convolutional neural networks (2016)
Simonyan, K., Brock, A., Donahue, J.: Large scale GAN training for high fidelity natural image synthesis (2018)
Laine, S., Lehtinen, J., Karras, T., Aila, T.: Progressive growing of GANs for improved quality, stability, and variation (2018)
Marra, F., Gragnaniello, D., Cozzolino, D.: Are GAN generated images easy to detect? A critical analysis of the state-of-the-art (2021)
Wang, Z.J., Montoya, E., Munechika, D., Yang, H., Hoover, B., Chau, D.H.: DiffusionDB: a large-scale prompt gallery dataset for text-to-image generative models. arXiv:2210.14896 [cs] (2022)
Fried, O., Sinitsa, S.: Deep image fingerprint: accurate and low budget synthetic image detector (2023)
Zhang, R., Wang, S.-Y., Wang, O.: CNN-generated images are surprisingly easy to spot... for now (2020)
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Das, S., Dutta, D., Ghosh, T., Naskar, R. (2023). Universal Detection and Source Attribution of Diffusion Model Generated Images with High Generalization and Robustness. In: Maji, P., Huang, T., Pal, N.R., Chaudhury, S., De, R.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2023. Lecture Notes in Computer Science, vol 14301. Springer, Cham. https://doi.org/10.1007/978-3-031-45170-6_45
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