{"@context":{"@language":"en","@vocab":"https://schema.org/","citeAs":"cr:citeAs","column":"cr:column","conformsTo":"dct:conformsTo","cr":"http://mlcommons.org/croissant/","rai":"http://mlcommons.org/croissant/RAI/","data":{"@id":"cr:data","@type":"@json"},"dataType":{"@id":"cr:dataType","@type":"@vocab"},"dct":"http://purl.org/dc/terms/","examples":{"@id":"cr:examples","@type":"@json"},"extract":"cr:extract","field":"cr:field","fileProperty":"cr:fileProperty","fileObject":"cr:fileObject","fileSet":"cr:fileSet","format":"cr:format","includes":"cr:includes","isLiveDataset":"cr:isLiveDataset","jsonPath":"cr:jsonPath","key":"cr:key","md5":"cr:md5","parentField":"cr:parentField","path":"cr:path","recordSet":"cr:recordSet","references":"cr:references","regex":"cr:regex","repeated":"cr:repeated","replace":"cr:replace","sc":"https://schema.org/","separator":"cr:separator","source":"cr:source","subField":"cr:subField","transform":"cr:transform","wd":"https://www.wikidata.org/wiki/"},"@type":"sc:Dataset","conformsTo":"http://mlcommons.org/croissant/1.0","name":""NeuroRenderedFake: A Challenging Benchmark to Detect Fake Images Generated by Advanced Neural Rendering Methods," Advances in Neural Information Processing Systems, 2025.","url":"https://doi.org/10.7910/DVN/BRUMCG","creator":[{"@type":"Person","affiliation":{"@type":"Organization","name":"The Hong Kong Polytechnic University"},"name":"Chengdong Dong"},{"@type":"Person","givenName":"Vijayakumar","familyName":"Bhagavatula","affiliation":{"@type":"Organization","name":"Carnegie Mellon University"},"name":"Vijayakumar Bhagavatula"},{"@type":"Person","affiliation":{"@type":"Organization","name":"The Hong Kong Polytechnic University"},"name":"Zhenyu Zhou"},{"@type":"Person","givenName":"Ajay","familyName":"Kumar","affiliation":{"@type":"Organization","name":"The Hong Kong Polytechnic University"},"name":"Ajay Kumar"}],"description":"The remarkable progress in neural-network-driven visual data generation, especially with neural rendering techniques like Neural Radiance Fields and 3D Gaussian splatting, offers a powerful alternative to GANs and diffusion models. These meth- ods can generate high-fidelity images and lifelike avatars, highlighting the need for robust detection methods. However, the lack of any large dataset containing images from neural rendering methods becomes a bottleneck for the detection of such sophisticated fake images. To address this limitation, we introduce NeuroRendered- Fake, a comprehensive benchmark for evaluating emerging fake image detection methods. Our key contributions are threefold: (1) A large-scale dataset of fake images synthesized using state-of-the-art neural rendering techniques, significantly expanding the scope of fake image detection beyond generative models; (2) A cross-domain evaluation protocol designed to assess the domain gap and common artifacts between generative and neural rendering-based fake images; and (3) An in-depth spectral energy analysis that reveals how frequency domain characteris- tics influence the performance of fake image detectors. We train representative detectors, based on spatial, spectral, and multimodal architectures, on fake images generated by both generative and neural rendering models. We evaluate these detectors on 15 groups of fake images synthesized by cutting-edge neural render- ing models, generative models, and combined methods that can exhibit artifacts from both domains. Additionally, we provide insightful findings through detailed experiments on degraded fake image detection and the impact of spectral features, aiming to advance research in this critical area.","keywords":["Computer and Information Science"],"license":"http://creativecommons.org/publicdomain/zero/1.0","datePublished":"2025-05-16","dateModified":"2025-10-23","includedInDataCatalog":{"@type":"DataCatalog","name":"Harvard Dataverse","url":"https://dataverse.harvard.edu"},"publisher":{"@type":"Organization","name":"Harvard Dataverse"},"version":"2.1","citeAs":"@data{DVN/BRUMCG_2025,author = {Chengdong Dong and Vijayakumar Bhagavatula and Zhenyu Zhou and Ajay Kumar},publisher = {Harvard Dataverse},title = {\"NeuroRenderedFake: A Challenging Benchmark to Detect Fake Images Generated by Advanced Neural Rendering Methods,\" Advances in Neural Information Processing Systems, 2025.},year = {2025},url = {https://doi.org/10.7910/DVN/BRUMCG}}","citation":[{"@type":"CreativeWork","name":"Chengdong Dong, Vijayakumar Bhagavatula, Zhenyu Zhou, and Ajay Kumar. \"NeuroRenderedFake: A Challenging Benchmark to Detect Fake Images Generated by Advanced Neural Rendering Methods,\" Advances in Neural Information Processing Systems, 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