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Existing face forgery detection models typically attempt to discriminate fake images by identifying either spatial artifacts (e.g., generative distortions and blending inconsistencies) or predominantly frequency\u2010based artifacts (e.g., GAN fingerprints). However, a singular focus on a single type of forgery cue can lead to limited model performance. In this work, we propose a novel cross\u2010domain approach that leverages a combination of both spatial and frequency\u2010aware cues to enhance deepfake detection. First, we extract wavelet features using wavelet transformation and residual features using a specialized frequency domain filter. These complementary feature representations are then concatenated to obtain a composite frequency domain feature set. Furthermore, we introduce an adaptive feature fusion module that integrates the RGB color features of the image with the composite frequency domain features, resulting in a rich, multifaceted set of classification features. Extensive experiments conducted on benchmark deepfake detection datasets demonstrate the effectiveness of our method. Notably, the accuracy of our method on the challenging FF++ dataset is mostly above 98%, showcasing its strong performance in reliably identifying deepfake images across diverse forgery techniques.<\/jats:p>","DOI":"10.1155\/2024\/7578036","type":"journal-article","created":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T11:35:02Z","timestamp":1730979302000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Deepfake Detection Based on the Adaptive Fusion of Spatial\u2010Frequency Features"],"prefix":"10.1155","volume":"2024","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3920-0264","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-1123-9294","authenticated-orcid":false,"given":"Qile","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-5871-9607","authenticated-orcid":false,"given":"Botao","family":"Jing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3784-0818","authenticated-orcid":false,"given":"Yeling","family":"Tang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9933-7900","authenticated-orcid":false,"given":"Zengren","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1057-7973","authenticated-orcid":false,"given":"Bo","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,11,7]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/tcsvt.2022.3207310"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/lsp.2024.3365034"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"ZhangY. 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