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...
The rapid advancement of the deep generative models has enabled the creation of highly realistic deepfake videos capable of manipulating facial content with high visual fidelity. Detecting such manipulations becomes more challenging when videos are stored or transmitted in compressed formats, as compression alters pixe...
Deepfake technology poses a critical threat to live video conferencing and biometric authentication. Existing detection models are either purely spatial—rendering them vulnerable to video compression—or rely on computationally heavy 3D-CNNs incompatible with strict real-time CPU latency constraints. We propose a highly...
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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