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Frequency-Domain Artefact

Definition

A periodic or statistical anomaly in the Fourier spectrum of an image or audio signal introduced by the generation pipeline's upsampling, filter, or codec operations. The GAN checkerboard pattern is the canonical example.

Related terms

CLIP-Based Detection
Detection approach using OpenAI CLIP or similar vision-language foundation models as a feature extractor. The broad pre-training enables generalisation to generation methods...
CNN Residual Detector
A convolutional neural network trained on the high-frequency residual image, the difference between the original and a de-noised version, to classify whether...
Detection Generalisation
The capacity of a trained detector to correctly identify deepfakes produced by generators not seen during training. Low generalisation is the central...
FaceForensics++
A video dataset released by Rossler et al. (2019) containing 1000+ YouTube videos manipulated by four methods: DeepFakes, Face2Face, FaceSwap, and NeuralTextures....
Frequency-Domain Analysis
Detection approach that transforms image patches into the frequency domain (DCT or FFT) to expose periodic artifacts introduced by upsampling layers in...
Generalisation Gap
The drop in detection accuracy when a classifier trained on one generation method is applied to a different method. Caused by learning...
Noiseprint
A CNN-based camera-model fingerprint extractor by Cozzolino and Verdoliva. Applied to deepfakes, it reveals inconsistency between the camera fingerprint in the genuine...
Physiological Signal
A biological process visible in video, such as eye blinking, rPPG (remote photoplethysmography), and head micro-motion from the cardiac cycle, that deepfake...
Remote Photoplethysmography (rPPG)
A technique that detects the pulse-driven skin-colour variation in a face video without contact sensors. In real video, this signal is present...
rPPG
Remote photoplethysmography. A technique for measuring heart rate from subtle periodic colour changes in facial skin caused by blood-volume pulses. Authentic video...
XceptionNet
A depthwise-separable convolutional architecture proposed by Rossler et al. as the baseline binary classifier in FaceForensics++. Trained to distinguish real from manipulated...

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