Browse State-of-the-Art › Image and Video Forgery Detection
Image and Video Forgery Detection
4 papers with code · 0 benchmarks · 0 datasets archive 2025-07-28
Benchmarks archive 2025-07-28
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Datasets archive 2025-07-28
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Most implemented papers archive 2025-07-28
4 shown of 4 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.
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26 Oct 2018 3 repositories listedRecent advances in media generation techniques have made it easier for attackers to create forged images and videos.
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28 Oct 2019 2 repositories listed Syntology ran 0 of 3 samples · 3 unverifiedIn this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning.
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14 Apr 2023 1 repository listedTo achieve this, we propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt.
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1 Jan 2023 1 repository listedIn this paper, we propose a novel framework to capture more fine-grained clues in complex scenarios for tampered text detection, termed as Document Tampering Detector (DTD), which consists of a Frequency Perception Head…
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.
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