{"url":"/dataset/cifake-real-and-ai-generated-synthetic-images","name":"CIFAKE: Real and AI-Generated Synthetic Images","full_name":null,"description_markdown":"The quality of AI-generated images has rapidly increased, leading to concerns of authenticity and trustworthiness.\r\n\r\nCIFAKE is a dataset that contains 60,000 synthetically-generated images and 60,000 real images (collected from CIFAR-10). Can computer vision techniques be used to detect when an image is real or has been generated by AI?\r\n\r\n##Dataset details\r\nThe dataset contains two classes - REAL and FAKE.\r\nFor REAL, we collected the images from Krizhevsky & Hinton's CIFAR-10 dataset\r\nFor the FAKE images, we generated the equivalent of CIFAR-10 with Stable Diffusion version 1.4\r\nThere are 100,000 images for training (50k per class) and 20,000 for testing (10k per class)\r\n\r\n##References\r\nIf you use this dataset, you must cite the following sources\r\n\r\nKrizhevsky, A., & Hinton, G. (2009). Learning multiple layers of features from tiny images.\r\n\r\nBird, J.J., Lotfi, A. (2023). CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images. arXiv preprint arXiv:2303.14126.\r\n\r\nReal images are from Krizhevsky & Hinton (2009), fake images are from Bird & Lotfi (2023). The Bird & Lotfi study is a preprint currently available on ArXiv and this description will be updated when the paper is published.\r\n\r\n##License\r\nThis dataset is published under the same MIT license as CIFAR-10:\r\n\r\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\r\n\r\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.","description_withheld":null,"homepage":"https://www.kaggle.com/datasets/birdy654/cifake-real-and-ai-generated-synthetic-images","introduced_date":"2023-03-24","introduced_date_note":null,"introduced_by":null,"license":{"name":"MIT","url":"https://github.com/wichtounet/cifar-10/blob/master/LICENSE"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"DeepFake Detection","url":"/task/deepfake-detection","datasets_with_task":"/datasets/task/deepfake-detection"}],"languages":[],"variants":["CIFAKE: Real and AI-Generated Synthetic Images"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-cifake-real-and-ai","task":"Classification","dataset_variant":"CIFAKE: Real and AI-Generated Synthetic Images","rows":1,"metrics":["Validation Accuracy"],"first_row_in_archive_order":{"model":"CNN","paper":"/paper/cifake-image-classification-and-explainable","metrics":{"Validation Accuracy":"93.55"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/deepfake-detection-on-cifake-real-and-ai","task":"DeepFake Detection","dataset_variant":"CIFAKE: Real and AI-Generated Synthetic Images","rows":1,"metrics":["AUC","Validation Accuracy"],"first_row_in_archive_order":{"model":"FasterThanLies","paper":"/paper/faster-than-lies-real-time-deepfake-detection","metrics":{"AUC":"99.65","Validation Accuracy":"97.29"},"code_links":[{"title":"fedeloper/binary_deepfake_detection","url":"https://github.com/fedeloper/binary_deepfake_detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/faster-than-lies-real-time-deepfake-detection","title":"Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks","date":"2024-06-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cifake-image-classification-and-explainable","title":"CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images","date":"2023-03-24","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}