AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This work proposes a spatial-temporal model with two key components: one targeting artifacts within individual frames and the other analyzing inconsistencies across consecutive frames, both leveraging a bidirectional long short-term memory (Bi-LSTM) mechanism.
This work proposes a parameter-efficient, video-based deepfake detection framework that leverages a frozen foundation model encoder coupled with a lightweight spatio-temporal decoder, complemented by a Bidirectional Spatio-Temporal decoder that models local temporal transitions and bidirectional temporal dependencies a...
BlinkNet, an explainable deepfake-detection framework that examines the spatial appearance and temporal kinematics of eye blinks, indicates that ocular dynamics can complement spatial evidence while improving efficiency and interpretability.
Deepfake videos generated using modern deep learning techniques pose significant threats to digital media authenticity and public trust. These manipulated videos often appear highly realistic, making manual verification difficult. This paper proposes an efficient spatiotemporal deepfake detection framework that combine...
A compression-aware multimodal deepfake detection framework that jointly exploits spatial, frequency and motion information from compressed videos and achieves an accuracy level of 99.63% and Area Under Curve of 99.83% on the FaceForensics++ (FF++) dataset, improving robustness to compression artifacts and enhances dee...
A highly optimized Two-Stream Spatio-Temporal architecture specifically designed for zero-latency live video evaluation, which extracts fine-grained spatial artifacts using a lightweight EfficientNet-B0 backbone, while a unidirectional Gated Recurrent Unit (GRU) models frame-to-frame physiological inconsistencies.
A detection framework is proposed that extracts per-video rPPG wave- forms via RhythmFormer and trains a suite of lightweight classifiers to distinguish real from synthesized physiologi- cal signals and shows that detec- tion difficulty is strongly method-dependent.
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