TL;DR
WTMSA-Net uses a Wavelet Transform-based Multi-Stage Attention Network to thoroughly analyze the spatial and frequency characteristics of images, enhancing the model’s ability to identify intricate deepfake manipulations and enhancing the model’s ability to identify intricate deepfake manipulations.
FAViT (Frequency-Aware Vision Transformer), a hybrid architecture capable of jointly utilizing spatial- and frequency-domain forensic information by the means of a bidirectional cross-attention fusion scheme, is presented.
This work explores an approach that integrates wavelet-based frequency analysis with deep learning to enhance deepfake detection, and suggests that wavelet sub-bands expose manipulation cues that are useful for detecting unseen fake classes, but they should not be interpreted as a uniform robustness improvement.
LGF-Net is proposed, a unified framework that jointly models spatial semantics and adaptive spectral cues for deepfake detection and achieves competitive intra-dataset and cross-dataset performance compared with several state-of-the-art deepfake detection methods.
The model innovatively combines a one-dimensional convolutional autoencoder and a multi-layer perceptron to extract local temporal transient features and global variable spatial mappings, respectively, and an FFT-Attention module is introduced to enhance adaptive focus on low-frequency spectral modes while suppressing...
EMBNet is proposed, a task-oriented deepfake speech detection framework that integrates efficient channel attention (ECA) with a multi-scale bottleneck to enhance the representation of subtle acoustic anomalies and may support forensic audio authenticity assessment by improving the discrimination between genuine and ma...
An advanced pre-processing pipeline and the detection model for improving the Deepfake detection in videos is developed and the Hybrid Localized Artifact Adaptively Weighted Multi-scale Attention Network-Spatio-Temporal Hypergraph Optimized Convolutional Vision Transformer Networks is developed for Deepfake video Detec...
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