Advances in deepfake research have led to the creation of almost perfect manipulations undetectable by human eyes and some deepfakes detection tools. Recently, several techniques have been proposed to differentiate deepfakes from realistic images and videos. This paper introduces a Frequency Enhanced Self-Blended Images (FSBI) approach for deepfakes detection. This proposed approach utilizes Discrete Wavelet Transforms (DWT) to extract discriminative features from the self-blended images (SBI) to be used for training a convolutional network architecture model. The SBIs blend the image with itself by introducing several forgery artifacts in a copy of the image before blending it. This prevents the classifier from overfitting specific artifacts by learning more generic representations. These blended images are then fed into the frequency features extractor to detect artifacts that can not be detected easily in the time domain. The proposed approach has been evaluated on FF++ and Celeb-DF datasets and the obtained results outperformed the state-of-the-art techniques with the cross-dataset evaluation protocol.

这篇论文介绍了一种用于检测深度伪造的频率增强自混合图像（FSBI）方法，通过利用离散小波变换（DWT）从自混合图像（SBI）中提取判别特征，训练卷积网络模型，在频域中检测时间域中难以检测到的伪造痕迹，并在FF++和Celeb-DF数据集上的跨数据集评估中表现优于现有技术。

FSBI: 增频自混合图像的 Deepfakes 检测