{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beyond-the-spectrum-detecting-deepfakes-via","title":"Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis","arxiv_id":"2105.14376","date":"2021-05-29","proceeding":null,"authors":["Yang He","Ning Yu","Margret Keuper","Mario Fritz"],"abstract":"The rapid advances in deep generative models over the past years have led to highly {realistic media, known as deepfakes,} that are commonly indistinguishable from real to human eyes. These advances make assessing the authenticity of visual data increasingly difficult and pose a misinformation threat to the trustworthiness of visual content in general. Although recent work has shown strong detection accuracy of such deepfakes, the success largely relies on identifying frequency artifacts in the generated images, which will not yield a sustainable detection approach as generative models continue evolving and closing the gap to real images. In order to overcome this issue, we propose a novel fake detection that is designed to re-synthesize testing images and extract visual cues for detection. The re-synthesis procedure is flexible, allowing us to incorporate a series of visual tasks - we adopt super-resolution, denoising and colorization as the re-synthesis. We demonstrate the improved effectiveness, cross-GAN generalization, and robustness against perturbations of our approach in a variety of detection scenarios involving multiple generators over CelebA-HQ, FFHQ, and LSUN datasets. Source code is available at https://github.com/SSAW14/BeyondtheSpectrum.","url_abs":"https://arxiv.org/abs/2105.14376v1","url_pdf":"https://arxiv.org/pdf/2105.14376v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beyond-the-spectrum-detecting-deepfakes-via","repo_url":"https://github.com/SSAW14/BeyondtheSpectrum","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"colorization","task_name":"Colorization"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":"super-resolution","task_name":"Super-Resolution"}],"methods":[{"method_slug":"colorization","method_name":"Colorization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2105.14376","atlas_url":"https://app.syntology.ai/?focus=2105.14376","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14376"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"deterministic:regex_extraction","url":"https://github.com/SSAW14/BeyondtheSpectrum","reach":null}],"summary":{"ran":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"bbe6241559305741","entry":"DenseLayer","repo":"SSAW14/BeyondtheSpectrum","repo_kind":"official","path":"sr_models/model.py","file_url":"https://github.com/SSAW14/BeyondtheSpectrum/blob/HEAD/sr_models/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"bbe6241559305741"}},{"code_sha256_prefix":"0afb0adea7879e86","entry":"RDB","repo":"SSAW14/BeyondtheSpectrum","repo_kind":"official","path":"sr_models/model.py","file_url":"https://github.com/SSAW14/BeyondtheSpectrum/blob/HEAD/sr_models/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0afb0adea7879e86"}},{"code_sha256_prefix":"8a4657565eaa9d9e","entry":"RDN","repo":"SSAW14/BeyondtheSpectrum","repo_kind":"official","path":"sr_models/model.py","file_url":"https://github.com/SSAW14/BeyondtheSpectrum/blob/HEAD/sr_models/model.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8a4657565eaa9d9e"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}