METHODS AND APPARATUS FOR DEEPFAKE DETECTION WITH MULTI-SCALE FEATURE PROCESSING AND LOCAL VISUAL DESCRIPTORS
개요
발명자
Anthony Rhodes; Allan Mora Brenes; Sangita Ravi Sharma; Lama Nachman
IPC 분류
CPC 분류
Deepfake detection is performed using Multi-Scale Local Descriptor (MSLD) augmentation. The MSLD-based augmentation improves the robustness and generalizability of PPG-based deepfake detection pipelines across a variety of real-world deepfake datasets. Multiscale local descriptor PPG-based features encode blood volume changes across multiple spatial scales in parallel using local binary patterns. A full set of multi-scale PPG maps derived from raw region-of-interest (ROI) images associated with an input video is concatenated with multi-scale local descriptor PPG maps into a single input tensor. The resulting output from the single input tensor is passed to a deepfake detection classifier for classification of the input video as an authentic video or a deepfake.
원문 (중국어)
Deepfake detection is performed using Multi-Scale Local Descriptor (MSLD) augmentation. The MSLD-based augmentation improves the robustness and generalizability of PPG-based deepfake detection pipelines across a variety of real-world deepfake datasets. Multiscale local descriptor PPG-based features encode blood volume changes across multiple spatial scales in parallel using local binary patterns. A full set of multi-scale PPG maps derived from raw region-of-interest (ROI) images associated with an input video is concatenated with multi-scale local descriptor PPG maps into a single input tensor. The resulting output from the single input tensor is passed to a deepfake detection classifier for classification of the input video as an authentic video or a deepfake.