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description=FakeInversion: Learning to Detect Images from Unseen Text-to-Image Models by Inverting Stable Diffusion;
keywords=diffusion, deepfake, detection, inversion, ddim;

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the (20), images (16), fake (12), and (10), from (6), model (6), this (5), real (5), cvpr (4), image (4), that (4), inversion (4), #detect (4), #stable (4), diffusion (4), new (4), test (4), laion (4), source (3), are (3), for (3), models (3), pixart (3), kandinsky (3), midjourney (3), use (3), our (3), style (3), code (2), generated (2), detectors (2), generative (2), cazenavette (2), george (2), sud (2), avneesh (2), leung (2), thomas (2), usman (2), ben (2), learning (2), unseen (2), inverting (2), 2024 (2), sdxl (2), playground (2), dall (2), than (2), generator (2), evaluation (2), methods (2), genai (2), only (2), set (2), despite (2), question (2), well (2), pre (2), using (2), features (2), etc (2), training (2), detection (2), input (2), original (2), website, based, wang, cnn, surprisingly, easy, spot, now, 2022, ojha, towards, universal, generalize, across, 2023, references, article, cazenavette2024fake, author, title, journal, year, bibtex, wurstchen, segmind, vega, cascade, ssd, seg, moe, dpo, please, following, links, view, previews, sets, left, corresponding, found, via, reverse, search, right, browsing, when, collected, way, distribution, much, closer, existing, samples, typically, having, very, different, styles, see, above, unfortunately, often, results, mismatch, between, calling, into, whether, not, actually, detecting, realness, merely, some, kind, bias, help, alleviate, these, concerns, introduce, benchmarks, intended, show, how, would, perform, wild, searching, internet, most, closely, match, content, obtained, dalle, paintings, liu, cheng, fine, art, you, painting, benchmark, extracted, weak, method, generalizes, remarkably, can, modern, propriatary, imagen, open, generators, were, never, seen, during, prior, learn, simply, classifier, wish, leverage, trained, train, better, obtain, additional, then, together, reconstructed, inverted, ddim, gen, detector, data, arxiv, google, research, massachusetts, institute, technology,

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fake inversion fake inversion learning to detect images from unseen models by inverting stable diffusion cvpr 2024 george cazenavette avneesh sud thomas leung ben usman massachusetts institute of technology google research arxiv data code a new gen ai detector prior fake image detection methods 1 2 learn to detect fake images by simply training a classifier on the original images we wish to leverage a pre trained diffusion model stable diffusion 1 5 to train a better detection model to do this we obtain additional input features by using ddim inversion and then use the original inverted reconstructed together as the input to our model despite only using features extracted from a weak generator sd v1 5 our method generalizes remarkably well and can detect fake images from modern propriatary dall e 3 midjourney imagen etc and open source kandinsky pixart α etc generators that were never seen during training a new evaluation benchmark laion paintings by 刘诚 liu cheng fine art and you painting dalle 3 midjourney v6 pixart α playground 2 5 existing evaluation methods for genai detectors only use laion images as the real samples in the test set despite generated fake images typically having very different styles than the real laion images see above unfortunately this often results in a style mismatch between the real and fake test images calling into question whether or not the model is actually detecting realness or merely some kind of style bias to help alleviate these concerns we introduce a new set of benchmarks intended to show how well a model would perform in the wild we do this by searching the pre genai internet for images that most closely match the style and content of the fake images obtained from the generative model in question when collected this way the distribution of real test images is much closer than laion to that of the fake images from the generator image browsing please use the following links to view previews of our new test sets fake images are on the left and the corresponding real images found via reverse image search are on the right dall e 3 midjourney kandinsky 2 kandinsky 3 pixart α playground 2 5 sdxl sdxl dpo seg moe ssd 1b stable cascade segmind vega wurstchen 2 bibtex article cazenavette2024fake author cazenavette george and sud avneesh and leung thomas and usman ben title fake inversion learning to detect images from unseen models by inverting stable diffusion journal cvpr year 2024 references 1 wang et al cnn generated images are surprisingly easy to spot for now cvpr 2022 2 ojha et al towards universal fake image detectors that generalize across generative models cvpr 2023 website source based on this source code