{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T12:35:42Z","timestamp":1785328542910,"version":"3.55.0"},"publisher-location":"Cham","reference-count":70,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031730320","type":"print"},{"value":"9783031730337","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:00:00Z","timestamp":1730332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-3-031-73033-7_16","type":"book-chapter","created":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:03:55Z","timestamp":1730333035000},"page":"282-301","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Which Model Generated This Image? A Model-Agnostic Approach for\u00a0Origin Attribution"],"prefix":"10.1007","author":[{"given":"Fengyuan","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haochen","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yiming","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip","family":"Torr","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jindong","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,31]]},"reference":[{"key":"16_CR1","unstructured":"Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214\u2013223. PMLR (2017)"},{"key":"16_CR2","unstructured":"Betker, J., et\u00a0al.: Improving image generation with better captions. Comput. Sci. 2(3), 8 (2023). https:\/\/cdn.openai.com\/papers\/dall-e-3.pdf"},{"key":"16_CR3","unstructured":"BIDEN, J.R.: Executive order on the safe, secure, and trustworthy development and use of artificial intelligence (2023). https:\/\/www.whitehouse.gov\/briefing-room\/presidential-actions\/2023\/10\/30\/executive-order-on-the-safe-secure-and-trustworthy-development-and-use-of-artificial-intelligence\/"},{"key":"16_CR4","doi-asserted-by":"publisher","unstructured":"Chandrasegaran, K., Tran, NT., Binder, A., Cheung, N.M.: Discovering transferable forensic features for CNN-generated images detection. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. ECCV 2022. LNCS, vol. 13675, pp. 671\u2013689. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19784-0_39","DOI":"10.1007\/978-3-031-19784-0_39"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Changpinyo, S., Sharma, P., Ding, N., Soricut, R.: Conceptual 12M: pushing web-scale image-text pre-training to recognize long-tail visual concepts. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 3558\u20133568 (2021)","DOI":"10.1109\/CVPR46437.2021.00356"},{"key":"16_CR6","doi-asserted-by":"crossref","unstructured":"Chen, J., Sathe, S., Aggarwal, C., Turaga, D.: Outlier detection with autoencoder ensembles. In: Proceedings of the 2017 SIAM International Conference on Data Mining, pp. 90\u201398. SIAM (2017)","DOI":"10.1137\/1.9781611974973.11"},{"key":"16_CR7","unstructured":"Chen, S., Gu, J., Han, Z., Ma, Y., Torr, P., Tresp, V.: Benchmarking robustness of adaptation methods on pre-trained vision-language models. In: Advances in Neural Information Processing Systems. vol. 36 (2024)"},{"key":"16_CR8","unstructured":"Cheng, H., et al.: Unveiling typographic deceptions: Insights of the typographic vulnerability in large vision-language model. arXiv. org"},{"key":"16_CR9","doi-asserted-by":"crossref","unstructured":"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\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16_CR10","unstructured":"Ding, Y., Thakur, N., Li, B.: Does a GAN leave distinct model-specific fingerprints. In: Proceedings of the BMVC (2021)"},{"key":"16_CR11","unstructured":"Dosovitskiy, A., et\u00a0al.: An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Frikha, A., Krompa\u00df, D., K\u00f6pken, H.G., Tresp, V.: Few-shot one-class classification via meta-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol.\u00a035, pp. 7448\u20137456 (2021)","DOI":"10.1609\/aaai.v35i8.16913"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"Girish, S., Suri, S., Rambhatla, S.S., Shrivastava, A.: Towards discovery and attribution of open-world GAN generated images. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 14094\u201314103 (2021)","DOI":"10.1109\/ICCV48922.2021.01383"},{"issue":"11","key":"16_CR14","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM 63(11), 139\u2013144 (2020)","journal-title":"Commun. ACM"},{"key":"16_CR15","unstructured":"Gu, J.: Responsible generative AI: What to generate and what not. arXiv preprint arXiv:2404.05783 (2024)"},{"key":"16_CR16","unstructured":"Gu, J., et al.: A systematic survey of prompt engineering on vision-language foundation models. arXiv preprint arXiv:2307.12980 (2023)"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Gu, J., Tresp, V.: Improving the robustness of capsule networks to image affine transformations. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7285\u20137293 (2020)","DOI":"10.1109\/CVPR42600.2020.00731"},{"key":"16_CR18","doi-asserted-by":"publisher","unstructured":"Gu, J., Tresp, V., Qin, Y.: Are vision transformers robust to patch perturbations?. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds) Computer Vision \u2013 ECCV 2022. ECCV 2022. LNCS, vol. 13672, pp. 404\u2013421. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19775-8_24","DOI":"10.1007\/978-3-031-19775-8_24"},{"key":"16_CR19","doi-asserted-by":"crossref","unstructured":"Gu, S., et al.: Vector quantized diffusion model for text-to-image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10696\u201310706 (2022)","DOI":"10.1109\/CVPR52688.2022.01043"},{"key":"16_CR20","doi-asserted-by":"crossref","unstructured":"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\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"16_CR21","first-page":"6840","volume":"33","author":"J Ho","year":"2020","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Adv. Neural. Inf. Process. Syst. 33, 6840\u20136851 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Jia, X., et al.: Revisiting and exploring efficient fast adversarial training via LAW: Lipschitz regularization and auto weight averaging. IEEE Trans. Inf. Forensics Secur. 19, 8125\u20138139 (2024)","DOI":"10.1109\/TIFS.2024.3420128"},{"key":"16_CR24","doi-asserted-by":"crossref","unstructured":"Kang, M., et al.: Scaling up GANs for text-to-image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10124\u201310134 (2023)","DOI":"10.1109\/CVPR52729.2023.00976"},{"key":"16_CR25","unstructured":"Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)"},{"key":"16_CR26","doi-asserted-by":"crossref","unstructured":"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\u20138119 (2020)","DOI":"10.1109\/CVPR42600.2020.00813"},{"key":"16_CR27","unstructured":"Kim, C., Ren, Y., Yang, Y.: Decentralized attribution of generative models. arXiv preprint arXiv:2010.13974 (2020)"},{"key":"16_CR28","doi-asserted-by":"crossref","unstructured":"Kingma, D.P., Welling, M., et\u00a0al.: An introduction to variational autoencoders. Found. Trends\u00ae Mach. Learn. 12(4), 307\u2013392 (2019)","DOI":"10.1561\/2200000056"},{"key":"16_CR29","unstructured":"Laszkiewicz, M., Ricker, J., Lederer, J., Fischer, A.: Single-model attribution via final-layer inversion. arXiv preprint arXiv:2306.06210 (2023)"},{"key":"16_CR30","unstructured":"LeCun, Y., Cortes, C., Burges, C., et\u00a0al.: MNIST handwritten digit database (2010)"},{"key":"16_CR31","doi-asserted-by":"crossref","unstructured":"Li, H., Shen, C., Torr, P., Tresp, V., Gu, J.: Self-discovering interpretable diffusion latent directions for responsible text-to-image generation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12006\u201312016 (2024)","DOI":"10.1109\/CVPR52733.2024.01141"},{"key":"16_CR32","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"16_CR33","doi-asserted-by":"crossref","unstructured":"Liu, R., Khakzar, A., Gu, J., Chen, Q., Torr, P., Pizzati, F.: Latent guard: a safety framework for text-to-image generation. arXiv preprint arXiv:2404.08031 (2024)","DOI":"10.1007\/978-3-031-73347-5_6"},{"key":"16_CR34","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhu, Y., Gu, J., Lan, Y., Yang, C., Qiao, Y.: MM-SafetyBench: A benchmark for safety evaluation of multimodal large language models. arXiv preprint arXiv:2311.17600 (2023)","DOI":"10.1007\/978-3-031-72992-8_22"},{"key":"16_CR35","doi-asserted-by":"publisher","unstructured":"Liu, X., et al.: Watermark vaccine: adversarial attacks to prevent watermark removal. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. ECCV 2022. LNCS, vol. 13674, pp. 1\u201317. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19781-9_1","DOI":"10.1007\/978-3-031-19781-9_1"},{"key":"16_CR36","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"issue":"1","key":"16_CR37","first-page":"187","volume":"5","author":"L Luo","year":"2009","unstructured":"Luo, L., Chen, Z., Chen, M., Zeng, X., Xiong, Z.: Reversible image watermarking using interpolation technique. IEEE Trans. Inf. Forensics Secur. 5(1), 187\u2013193 (2009)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"16_CR38","unstructured":"Van\u00a0der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(11), 2579\u20132605 (2008)"},{"key":"16_CR39","doi-asserted-by":"crossref","unstructured":"Mandelli, S., Bonettini, N., Bestagini, P., Tubaro, S.: Detecting GAN-generated images by orthogonal training of multiple CNNs. In: 2022 IEEE International Conference on Image Processing (ICIP), pp. 3091\u20133095. IEEE (2022)","DOI":"10.1109\/ICIP46576.2022.9897310"},{"key":"16_CR40","unstructured":"Nichol, A., et al.: GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. arXiv preprint arXiv:2112.10741 (2021)"},{"key":"16_CR41","doi-asserted-by":"crossref","unstructured":"Oussidi, A., Elhassouny, A.: Deep generative models: survey. In: 2018 International Conference on Intelligent Systems and Computer Vision (ISCV), pp.\u00a01\u20138. IEEE (2018)","DOI":"10.1109\/ISACV.2018.8354080"},{"key":"16_CR42","doi-asserted-by":"crossref","unstructured":"Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337\u20132346 (2019)","DOI":"10.1109\/CVPR.2019.00244"},{"issue":"6","key":"16_CR43","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1109\/83.846253","volume":"9","author":"S Pereira","year":"2000","unstructured":"Pereira, S., Pun, T.: Robust template matching for affine resistant image watermarks. IEEE Trans. Image Process. 9(6), 1123\u20131129 (2000)","journal-title":"IEEE Trans. Image Process."},{"key":"16_CR44","unstructured":"Radford, A., et\u00a0al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PMLR (2021)"},{"key":"16_CR45","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning, pp. 1278\u20131286. PMLR (2014)"},{"key":"16_CR46","doi-asserted-by":"crossref","unstructured":"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\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"16_CR47","doi-asserted-by":"crossref","unstructured":"Sabokrou, M., Khalooei, M., Fathy, M., Adeli, E.: Adversarially learned one-class classifier for novelty detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3379\u20133388 (2018)","DOI":"10.1109\/CVPR.2018.00356"},{"key":"16_CR48","first-page":"36479","volume":"35","author":"C Saharia","year":"2022","unstructured":"Saharia, C., et al.: Photorealistic text-to-image diffusion models with deep language understanding. Adv. Neural. Inf. Process. Syst. 35, 36479\u201336494 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR49","unstructured":"Sauer, A., Karras, T., Laine, S., Geiger, A., Aila, T.: StyleGAN-T: Unlocking the power of GANs for fast large-scale text-to-image synthesis. arXiv preprint arXiv:2301.09515 (2023)"},{"key":"16_CR50","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-319-59050-9_12","volume-title":"Information Processing in Medical Imaging","author":"T Schlegl","year":"2017","unstructured":"Schlegl, T., Seeb\u00f6ck, P., Waldstein, S.M., Schmidt-Erfurth, U., Langs, G.: Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Niethammer, M., et al. (eds.) IPMI 2017. LNCS, vol. 10265, pp. 146\u2013157. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-59050-9_12"},{"issue":"7","key":"16_CR51","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","volume":"13","author":"B Sch\u00f6lkopf","year":"2001","unstructured":"Sch\u00f6lkopf, B., Platt, J.C., Shawe-Taylor, J., Smola, A.J., Williamson, R.C.: Estimating the support of a high-dimensional distribution. Neural Comput. 13(7), 1443\u20131471 (2001)","journal-title":"Neural Comput."},{"key":"16_CR52","first-page":"25278","volume":"35","author":"C Schuhmann","year":"2022","unstructured":"Schuhmann, C., et al.: LAION-5B: an open large-scale dataset for training next generation image-text models. Adv. Neural. Inf. Process. Syst. 35, 25278\u201325294 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"16_CR53","unstructured":"Schuhmann, C., et al.: LAION-400M: Open dataset of clip-filtered 400 million image-text pairs. arXiv preprint arXiv:2111.02114 (2021)"},{"key":"16_CR54","doi-asserted-by":"crossref","unstructured":"Sha, Z., Li, Z., Yu, N., Zhang, Y.: DE-FAKE: detection and attribution of fake images generated by text-to-image generation models. In: Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security, pp. 3418\u20133432 (2023)","DOI":"10.1145\/3576915.3616588"},{"key":"16_CR55","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"key":"16_CR56","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)"},{"key":"16_CR57","doi-asserted-by":"crossref","unstructured":"Swanson, M.D., Zhu, B., Tewfik, A.H.: Transparent robust image watermarking. In: Proceedings of 3rd IEEE International Conference on Image Processing. vol.\u00a03, pp. 211\u2013214. IEEE (1996)","DOI":"10.1109\/ICIP.1996.560421"},{"key":"16_CR58","doi-asserted-by":"crossref","unstructured":"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\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"16_CR59","doi-asserted-by":"crossref","unstructured":"Tancik, M., Mildenhall, B., Ng, R.: StegaStamp: invisible hyperlinks in physical photographs. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2117\u20132126 (2020)","DOI":"10.1109\/CVPR42600.2020.00219"},{"key":"16_CR60","doi-asserted-by":"crossref","unstructured":"Tao, M., Bao, B.K., Tang, H., Xu, C.: GALIP: generative adversarial clips for text-to-image synthesis. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 14214\u201314223 (2023)","DOI":"10.1109\/CVPR52729.2023.01366"},{"key":"16_CR61","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., J\u00e9gou, H.: Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp. 10347\u201310357. PMLR (2021)"},{"key":"16_CR62","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., J\u00e9gou, H.: Going deeper with image transformers. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 32\u201342 (2021)","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"16_CR63","doi-asserted-by":"crossref","unstructured":"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 the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8695\u20138704 (2020)","DOI":"10.1109\/CVPR42600.2020.00872"},{"key":"16_CR64","unstructured":"Wang, Z., Chen, C., Zeng, Y., Lyu, L., Ma, S.: Alteration-free and model-agnostic origin attribution of generated images. arXiv preprint arXiv:2305.18439 (2023)"},{"key":"16_CR65","doi-asserted-by":"publisher","unstructured":"Wu, B., Gu, J., Li, Z., Cai, D., He, X., Liu, W.: Towards efficient adversarial training on vision transformers. In: Avidan, S., Brostow, G., Ciss\u00e9, M., Farinella, G.M., Hassner, T. (eds.) Computer Vision - ECCV 2022. ECCV 2022. LNCS, vol. 13673, pp. 307\u2013325. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-19778-9_18","DOI":"10.1007\/978-3-031-19778-9_18"},{"key":"16_CR66","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1162\/tacl_a_00166","volume":"2","author":"P Young","year":"2014","unstructured":"Young, P., Lai, A., Hodosh, M., Hockenmaier, J.: From image descriptions to visual denotations: new similarity metrics for semantic inference over event descriptions. Trans. Assoc. Computat. Linguist. 2, 67\u201378 (2014)","journal-title":"Trans. Assoc. Computat. Linguist."},{"key":"16_CR67","doi-asserted-by":"crossref","unstructured":"Yu, N., Davis, L.S., Fritz, M.: Attributing fake images to GANs: learning and analyzing GAN fingerprints. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 7556\u20137566 (2019)","DOI":"10.1109\/ICCV.2019.00765"},{"key":"16_CR68","doi-asserted-by":"crossref","unstructured":"Yu, N., Skripniuk, V., Abdelnabi, S., Fritz, M.: Artificial fingerprinting for generative models: Rooting deepfake attribution in training data. In: Proceedings of the IEEE\/CVF International conference on computer vision, pp. 14448\u201314457 (2021)","DOI":"10.1109\/ICCV48922.2021.01418"},{"key":"16_CR69","unstructured":"Yu, N., Skripniuk, V., Chen, D., Davis, L., Fritz, M.: Responsible disclosure of generative models using scalable fingerprinting. arXiv preprint arXiv:2012.08726 (2020)"},{"issue":"9","key":"16_CR70","doi-asserted-by":"publisher","first-page":"2337","DOI":"10.1007\/s11263-022-01653-1","volume":"130","author":"K Zhou","year":"2022","unstructured":"Zhou, K., Yang, J., Loy, C.C., Liu, Z.: Learning to prompt for vision-language models. Int. J. Comput. Vision 130(9), 2337\u20132348 (2022)","journal-title":"Int. J. Comput. Vision"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2024"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-73033-7_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,31]],"date-time":"2024-10-31T00:37:27Z","timestamp":1730335047000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-73033-7_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,31]]},"ISBN":["9783031730320","9783031730337"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-73033-7_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,31]]},"assertion":[{"value":"31 October 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Milan","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2024.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}