The term deepfake refers to all those multimedia contents that were
synthetically altered or created from scratch through the use of generative
models. This phenomenon has become widespread due to the use of increasingly
accurate and efficient architectures capable of rendering manipulated content
indistinguishable from real content. In order to fight the illicit use of this
powerful technology, it has become necessary to develop algorithms able to
distinguish synthetic content from real ones. In this study, a new algorithm
for the detection of deepfakes in digital videos is presented, focusing on the
main goal of creating a fast and explainable method from a forensic
perspective. To achieve this goal, the I-frames were extracted in order to
provide faster computation and analysis than approaches described in
literature. In addition, to identify the most discriminating regions within
individual video frames, the entire frame, background, face, eyes, nose, mouth,
and face frame were analyzed separately. From the Discrete Cosine Transform
(DCT), the Beta components were extracted from the AC coefficients and used as
input to standard classifiers (e.g., k-NN, SVM, and others) in order to
identify those frequencies most discriminative for solving the task in
question. Experimental results obtained on the Faceforensics++ and Celeb-DF
(v2) datasets show that the eye and mouth regions are those most discriminative
and able to determine the nature of the video with greater reliability than the
analysis of the whole frame. The method proposed in this study is analytical,
fast and does not require much computational power.

通过从离散余弦变换 (DCT) 中提取 Beta 分量并将其用作标准分类器的输入，从而快速识别合成内容并确定视频的性质，该方法在福全学的角度上具有又快又可解释性，不需要太多计算力。

提高视频深度伪造检测：一种基于 DCT 的补丁级分析方法

Improving Video Deepfake Detection: A DCT-Based Approach with  Patch-Level Analysis

This research evaluates a convolutional neural network (CNN) based approach
to forensic video steganalysis. A video steganography dataset is created to
train a CNN to conduct forensic steganalysis in the spatial domain. We use a
noise residual convolutional neural network to detect embedded secrets since a
steganographic embedding process will always result in the modification of
pixel values in video frames. Experimental results show that the CNN-based
approach can be an effective method for forensic video steganalysis and can
reach a detection rate of 99.96%. Keywords: Forensic, Steganalysis, Deep
Steganography, MSU StegoVideo, Convolutional Neural Networks

该研究评估了一种卷积神经网络（CNN）的方法来进行法医视频隐写分析，使用一个噪声残差的卷积神经网络来检测嵌入式的秘密，实验结果表明，CNN 方法可以是一种有效的法医视频隐写分析方法，可以达到 99.96% 的检测率。

利用噪声残差卷积神经网络进行空间域取证视频隐写分析

Forensic Video Steganalysis in Spatial Domain by Noise Residual  Convolutional Neural Network

Forensic analyses of digital images rely heavily on the traces of in-camera
and out-camera processes left on the acquired images. Such traces represent a
sort of camera fingerprint. If one is able to recover them, by suppressing the
high-level scene content and other disturbances, a number of forensic tasks can
be easily accomplished. A notable example is the PRNU pattern, which can be
regarded as a device fingerprint, and has received great attention in
multimedia forensics. In this paper we propose a method to extract a camera
model fingerprint, called noiseprint, where the scene content is largely
suppressed and model-related artifacts are enhanced. This is obtained by means
of a Siamese network, which is trained with pairs of image patches coming from
the same (label +1) or different (label -1) cameras. Although noiseprints can
be used for a large variety of forensic tasks, here we focus on image forgery
localization. Experiments on several datasets widespread in the forensic
community show noiseprint-based methods to provide state-of-the-art
performance.

本文提出了一种提取噪音指纹的相机模型指纹方法，用于图像取证，该方法通过 Siamese 网络来提取图片序列的差异进行相机指纹的提取，实验结果表明，该方法在图像取证方面表现出色。