Content-Invariant Spatio-Temporal Neural Framework for Forgery Detection in Image Sequences

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F26%3A0199661" target="_blank" >RIV/00216305:26220/26:0199661 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268794" target="_blank" >http://dx.doi.org/10.1109/ICUMT67815.2025.11268794</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICUMT67815.2025.11268794" target="_blank" >10.1109/ICUMT67815.2025.11268794</a>

Result language
angličtina
Original language name
Content-Invariant Spatio-Temporal Neural Framework for Forgery Detection in Image Sequences
Original language description
The increasing prevalence of deepfake videos underscores the need for effective and reliable detection methods. In this study, we propose a hybrid deepfake detection framework that integrates a static image forgery detector with a recurrent neural network (RNN) to exploit both spatial and temporal fea- tures. Specifically, we utilize an existing frame-level detector that identifies common forgery artifacts within individual frames. This is followed by a Long Short-Term Memory (LSTM) network that models temporal dependencies across frames, enabling detection of inconsistencies that are overlooked in frame-by-frame anal- ysis. Experimental results demonstrate that temporal modeling significantly improves accuracy over frame-level baselines. Our contributions are twofold: (i) we provide empirical evidence that deepfake videos exhibit detectable temporal signatures, and (ii) we construct a compact, real-world evaluation set of deepfake videos. Notably, detection performance on this dataset is lower than on standard benchmarks, suggesting a domain gap between commonly used training data and real-world deepfakes.
Czech name
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Czech description
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Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
International Conference on Ultra Modern Telecommunications and Workshops
ISBN
979-8-3315-7675-2
ISSN
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e-ISSN
2157-023X
Number of pages
5
Pages from-to
240-245
Publisher name
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Place of publication
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Event location
Florencie, Itálie
Event date
Nov 3, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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