Github Jeongsoop Community Forensics Project Page Https Jespark This repository contains the training and evaluation pipeline for community forensics. the pipeline supports distributed data parallel through torchrun and accepts two data sources hugging face repo and local data. I have been working on an intersection of computer vision, image processing, and image forensics. in my recent work community forensics, i study the generalization of fake image detectors by training them using images sampled from thousands of generators.
Github Jeongsoop Rgb No More An Official Code Release Of The Paper Project page: jespark projects 2024 community forensics community standards Β· jeongsoop community forensics. This repository contains the training and evaluation pipeline for community forensics. the pipeline supports distributed data parallel through torchrun and accepts two data sources hugging face repo and local data. the two data sources can be used on their own or can be combined. Our dataset contains 2.7m images generated by: (a) thousands of systematically downloaded open source text to image latent diffusion models, (b) manually chosen open source models with a variety of architectures, and (c) state of the art commercial models. we use this dataset to study generalization in the generated image detection problem. Using this dataset, we study the generalization abilities of fake image detectors. our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures.
Jeongsoop Jeongsoo Park Our dataset contains 2.7m images generated by: (a) thousands of systematically downloaded open source text to image latent diffusion models, (b) manually chosen open source models with a variety of architectures, and (c) state of the art commercial models. we use this dataset to study generalization in the generated image detection problem. Using this dataset, we study the generalization abilities of fake image detectors. our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures. Recent activity updated a dataset about 14 hours ago jespark communityforensics published a dataset about 17 hours ago jespark communityforensics updated a dataset about 18 hours ago jespark communityforensics view all activity. Vision transformer (vit) model trained on the largest dataset to date for detecting ai generated images in forensic applications. developed by: jeongsoo park and andrew owens, university of michigan. Using this dataset, we study the generalization abilities of fake image detectors. our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures. We use images sam pled from different numbers of open source latent diffusion models in our community forensics dataset to train fake image detectors (shown in fig. 2a).
Github Jaewookbyun Jaewookbyun Recent activity updated a dataset about 14 hours ago jespark communityforensics published a dataset about 17 hours ago jespark communityforensics updated a dataset about 18 hours ago jespark communityforensics view all activity. Vision transformer (vit) model trained on the largest dataset to date for detecting ai generated images in forensic applications. developed by: jeongsoo park and andrew owens, university of michigan. Using this dataset, we study the generalization abilities of fake image detectors. our experiments suggest that detection performance improves as the number of models in the training set increases, even when these models have similar architectures. We use images sam pled from different numbers of open source latent diffusion models in our community forensics dataset to train fake image detectors (shown in fig. 2a).
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